SO-YOLOv5: Small object recognition algorithm for sea cucumber in complex seabed environment

Underwater object recognition is a prerequisite for the realization of automated seafood harvesting. To solve the problems of low recognition accuracy and poor generalization ability of existing underwater small object, this paper takes sea cucumber as research objects to research the small object r...

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Published in:	Fisheries research Vol. 264; p. 106710
Main Authors:	Xuan, Kui, Deng, Limiao, Xiao, Ying, Wang, Peidong, Li, Juan
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.08.2023
Subjects:	algorithms aquaculture Aquatic animal Artificial Intelligence (AI) automation Bidirectional Feature Pyramid Network (BiFPN) Coordinate Attention (CA) data collection Deep learning Holothuroidea Object recognition Sea cucumber seafoods YOLOv5 Deep learning Sea cucumber Aquatic animal Artificial Intelligence (AI) YOLOv5 Object recognition Coordinate Attention (CA) Bidirectional Feature Pyramid Network (BiFPN)
ISSN:	0165-7836, 1872-6763
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Abstract	Underwater object recognition is a prerequisite for the realization of automated seafood harvesting. To solve the problems of low recognition accuracy and poor generalization ability of existing underwater small object, this paper takes sea cucumber as research objects to research the small object recognition approach in complex underwater environment. In this paper, a sea cucumber dataset is established to solve the problem of lacking sea cucumber dataset in the real seabed environment. A deep learning model SO-YOLOv5 is proposed based on YOLOv5 for underwater small object recognition, which improves the recognition ability of the algorithm for different sizes of objects in complex environment. The proposed model embeds a large-scale feature extraction layer in structure to increase the detection ability of small objects by referring to the idea of the Bi-directional Feature Pyramid Network. SO-YOLOv5 fuses the features between deep and shallow layers and balances the feature information of different scales, and integrates the Coordinate Attention mechanism to enhance the sensitivity of the algorithm to the direction and position information. The experimental results illustrate that the mAP of the proposed approach achieves 95.47% for sea cucumber recognition in the complex seabed environment, which is 3.42%, 6.79%, and 5.46% higher than the traditional Faster R-CNN, SSD, and YOLOv5 algorithms, respectively. This research not only provides an effective approach for sea cucumber recognition in complex environment but also has certain reference significance for the recognition of small objects in other complex environments. In addition, the proposed approach has practical application value to improve intelligence level in aquaculture. •A sea cucumbers dataset was established.•Feature extraction capability was enhanced through fusing large-size features to improve small object detection ability.•The deep and shallow layers of features are bi-directionally fused to balance the different scales of features.•The Coordinate Attention mechanism is embedded to enhance the algorithm’s sensitivity to the direction and location.
AbstractList	Underwater object recognition is a prerequisite for the realization of automated seafood harvesting. To solve the problems of low recognition accuracy and poor generalization ability of existing underwater small object, this paper takes sea cucumber as research objects to research the small object recognition approach in complex underwater environment. In this paper, a sea cucumber dataset is established to solve the problem of lacking sea cucumber dataset in the real seabed environment. A deep learning model SO-YOLOv5 is proposed based on YOLOv5 for underwater small object recognition, which improves the recognition ability of the algorithm for different sizes of objects in complex environment. The proposed model embeds a large-scale feature extraction layer in structure to increase the detection ability of small objects by referring to the idea of the Bi-directional Feature Pyramid Network. SO-YOLOv5 fuses the features between deep and shallow layers and balances the feature information of different scales, and integrates the Coordinate Attention mechanism to enhance the sensitivity of the algorithm to the direction and position information. The experimental results illustrate that the mAP of the proposed approach achieves 95.47% for sea cucumber recognition in the complex seabed environment, which is 3.42%, 6.79%, and 5.46% higher than the traditional Faster R-CNN, SSD, and YOLOv5 algorithms, respectively. This research not only provides an effective approach for sea cucumber recognition in complex environment but also has certain reference significance for the recognition of small objects in other complex environments. In addition, the proposed approach has practical application value to improve intelligence level in aquaculture. •A sea cucumbers dataset was established.•Feature extraction capability was enhanced through fusing large-size features to improve small object detection ability.•The deep and shallow layers of features are bi-directionally fused to balance the different scales of features.•The Coordinate Attention mechanism is embedded to enhance the algorithm’s sensitivity to the direction and location. Underwater object recognition is a prerequisite for the realization of automated seafood harvesting. To solve the problems of low recognition accuracy and poor generalization ability of existing underwater small object, this paper takes sea cucumber as research objects to research the small object recognition approach in complex underwater environment. In this paper, a sea cucumber dataset is established to solve the problem of lacking sea cucumber dataset in the real seabed environment. A deep learning model SO-YOLOv5 is proposed based on YOLOv5 for underwater small object recognition, which improves the recognition ability of the algorithm for different sizes of objects in complex environment. The proposed model embeds a large-scale feature extraction layer in structure to increase the detection ability of small objects by referring to the idea of the Bi-directional Feature Pyramid Network. SO-YOLOv5 fuses the features between deep and shallow layers and balances the feature information of different scales, and integrates the Coordinate Attention mechanism to enhance the sensitivity of the algorithm to the direction and position information. The experimental results illustrate that the mAP of the proposed approach achieves 95.47% for sea cucumber recognition in the complex seabed environment, which is 3.42%, 6.79%, and 5.46% higher than the traditional Faster R-CNN, SSD, and YOLOv5 algorithms, respectively. This research not only provides an effective approach for sea cucumber recognition in complex environment but also has certain reference significance for the recognition of small objects in other complex environments. In addition, the proposed approach has practical application value to improve intelligence level in aquaculture.
ArticleNumber	106710
Author	Deng, Limiao Wang, Peidong Li, Juan Xiao, Ying Xuan, Kui
Author_xml	– sequence: 1 givenname: Kui surname: Xuan fullname: Xuan, Kui organization: College of Mechanical and Electrical Engineering Qingdao Agricultural University, Qingdao 266109, China – sequence: 2 givenname: Limiao surname: Deng fullname: Deng, Limiao organization: College of Science and Information Qingdao Agricultural University, Qingdao 266109, China – sequence: 3 givenname: Ying surname: Xiao fullname: Xiao, Ying organization: School of Science, The Hong Kong University of Science and Technology, Hong Kong, China – sequence: 4 givenname: Peidong surname: Wang fullname: Wang, Peidong organization: Qingdao Product Quality Testing Research Institute, Qingdao 266101, China – sequence: 5 givenname: Juan surname: Li fullname: Li, Juan email: lijuan291@sina.com organization: College of Mechanical and Electrical Engineering Qingdao Agricultural University, Qingdao 266109, China
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Cites_doi	10.9728/dcs.2020.21.11.2011 10.1093/icesjms/fsab227 10.1016/j.compag.2021.106585 10.3390/s21010136 10.1109/TCYB.2019.2925015 10.1016/j.aquaeng.2022.102273 10.1016/j.compag.2018.08.018 10.1109/CVPR46437.2021.01350 10.1111/raq.12464 10.1109/CVPR42600.2020.01079 10.3390/s21103374 10.1016/j.compag.2022.106780 10.1109/ICSITech.2017.8257135 10.1007/s10499-021-00773-8 10.5755/j01.itc.50.1.25094 10.1016/j.compag.2019.01.012 10.1109/ICSES52305.2021.9633834 10.1007/s13042-021-01285-w 10.1016/j.mri.2020.08.013 10.21203/rs.2.24728/v1 10.1016/j.jff.2018.08.009 10.1016/j.engappai.2021.104190 10.1186/s43074-022-00066-0 10.1016/j.neucom.2022.01.099 10.1364/OE.25.022490 10.1111/2041-210X.13246 10.1109/ACCESS.2019.2962823 10.1109/SPW54247.2022.9833890 10.1109/TIP.2021.3074738 10.1088/1742-6596/2320/1/012028 10.1007/s10462-021-10102-3 10.1109/ACCESS.2019.2946062 10.1016/j.compag.2021.106135 10.1007/s10462-019-09719-2 10.1093/icesjms/fsz223 10.1145/3415048.3416098
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Keywords	Deep learning Sea cucumber Aquatic animal Artificial Intelligence (AI) YOLOv5 Object recognition Coordinate Attention (CA) Bidirectional Feature Pyramid Network (BiFPN)
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References	Darwish, Hassanien, Das (bib7) 2020; 53 Hoang Dinh, Turner, Smith, Wang, Nocillado, Palma, Suwansa-ard, Elizur, Cummins (bib16) 2019 Xu, Zhao, Li, Shang, Ding, Wang (bib59) 2022 Tian, Yang, Wang, Wang, Li, Liang (bib50) 2019; 157 Liu, Wang, Yuan, Zeng, Hone, Liu (bib32) 2021; 51 Redmon, Divvala, Girshick, Farhadi (bib41) 2016 Chen, Wu, Lin, Li, Chen, Yuan, Chen, Zou (bib6) 2022 Weihao, Shuhe, Sen, Bin (bib57) 2020; 2 Ahmad, ma, Yahya, Ahmad, Nazir, Haq, Ali (bib1) 2020 Qi, Liu, Liu, Xu, Guo, Tian, Li, Bao, Li (bib40) 2022; 194 Hau, Demetriou, Lupu (bib15) 2022 Yearbook, C.F.S. (2021): Fisheries Bureau of Ministry of Agriculture. Shi, Zhu, Lu (bib46) 2022 Zhang, Dong, Xu (bib66) 2022; 192 Ota, Umehara, Ishimaru, Ohno, Okamoto, Suzuki, Shirai, Ishida (bib38) 2017; Vol. 10133 Du, Jiao, Chu (bib8) 2022 Li, Xu, Jiang, Xiao, Deng, Han (bib26) 2020; 8 Karthi, M., Muthulakshmi, V., Priscilla, R., Praveen, P., & Vanisri, K., 2021. Evolution of YOLO-V5 Algorithm for Object Detection: Automated Detection of Library Books and Performace validation of Dataset. 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), 1–6. Lei, Tang, Li (bib21) 2022 Tian, Y., Lan, L., & Sun, L. (2020). A Review of Sonar Image Segmentation for Underwater Small Targets. Zhou, Song, Fu, Gao, Li, Cui (bib69) 2020 Althnian, AlSaeed, Al-Baity, Samha, Bin Dris, Alzakari, Abou Elwafa, Kurdi (bib3) 2021 . Yang, Zhang, Liu, Gao, Dong, Zhou (bib60) 2021; 13 Lei, Wang, Shen, Chen, Zhang (bib22) 2021 Mahmood, Bennamoun, An, Sohel, Boussaid, Hovey, Kendrick (bib36) 2020; 77 Gao, Fang, Sun, Wu, Zhao, Li, Li, Fu, Zhang (bib10) 2022 Li, Du (bib23) 2022; 55 Wang, Zheng, Zheng, Yu (bib54) 2017; 25 Unuma (bib52) 2002 Zeng, Sun, Zhu (bib62) 2021; 100 Mekhalfi, Nicolò, Bazi, Alrahhal, Alsharif, Al Maghayreh (bib37) 2021 Zeroual, Amroune, Derdour, Bentahar (bib63) 2021 Liu, Wang, Zeng, Alsaadi, Liu (bib33) 2021; 12 Wang, Zhao, Li, Liu, Xu, Li (bib56) 2022 Guo, Liu, Zhao, Cheng, Song, Gu, Zheng, Zheng (bib12) 2020 Lee, Lee, Park, Seo, Lee (bib20) 2020; 21 Li, X., & Liu, K. (2020). Restoration and Enhancement of Underwater Under-Exposure Images with Detail-Preserving. Zhang, Yu, Wen, Qi, Paul (bib65) 2021; 66 Liu, Jiang, Wang, Zheng, Zheng, Wang (bib30) 2022; 3 Han, Deng (bib14) 2018; 153 Shi, Li, Nie, Zhang, Wang (bib44) 2021; 50 Liu, Fan, Ouyang, Li (bib31) 2021; 21 Li, Jin, Fan, Zou, Chen, Li, Jiang, Liu (bib25) 2021; 21 Xu, Zhang, Wen (bib58) 2018; 49 Beilei, Bing, Yu, Yanan (bib4) 2013 Allken, Rosen, Handegard, Malde, Demer (bib2) 2021; 78 Li, Li, Yang, Zhang, Wang, Guo (bib27) 2022; 98 Ru, Zhang, Li, Liu, Yang (bib42) 2018 Liu, Fan, Chen (bib34) 2017 Setiawan, Wahyudin, Widianto (bib43) 2017 Ma, Xu, Jiang, Cheng, Yang, Shen, Yang, Huang, Ding, Yuan, Science (bib35) 2022 Peng, Miao, Li, Li (bib39) 2021 Zhao, Liu, Sun, Liu, Yang, Zhou (bib67) 2021; 30 Zhan, Li, Zhang (bib64) 2022; 483 Li, Quan, Song, Li, Yu, Li, Muhammad (bib24) 2021 Wang, Si, Qu, Gong, Cui (bib55) 2022; 2320 Hou, Q., Zhou, D., & Feng, J., 2021. Coordinate Attention for Efficient Mobile Network Design. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 13708–13717. Fu, Mandava, Keerthivasan, Li, Johnson, Martin, Altbach, Bilgin (bib9) 2020; 73 Shi, Liu, Niu, Yang, Huang (bib45) 2020 Tan, M., Pang, R., & Le, Q.V., 2020. EfficientDet: Scalable and Efficient Object Detection. In: In Proceedings of the IEEE conference on computer vision and pattern recognition, 10778–10787. Hu, Liu, Zhao, Liu, Yang, Sun, Chen, Li, Zhou (bib18) 2021; 185 Song, Wang, Huang, Liotta, Perra (bib47) 2020 Wang, Li, Wang, Xu, Zhang, Li (bib53) 2021; 29 Lin, He, Sun (bib29) 2019; 7 Cardenas, Camargo (bib5) 2018 Gray, Bierlich, Mantell, Friedlaender, Goldboge, Johnston (bib11) 2019; 10 Tan, M., Pang, R., & Le, Q. (2020). EfficientDet: Scalable and Efficient Object Detection. Guo, Guo, Jiang, Li, Gu, Zheng, Zheng, Wang (bib13) 2021 Zhao, Yang, Zhou, Sun, Ge, Liu (bib68) 2021 Chen (10.1016/j.fishres.2023.106710_bib6) 2022 Liu (10.1016/j.fishres.2023.106710_bib31) 2021; 21 Wang (10.1016/j.fishres.2023.106710_bib55) 2022; 2320 Guo (10.1016/j.fishres.2023.106710_bib13) 2021 Cardenas (10.1016/j.fishres.2023.106710_bib5) 2018 Li (10.1016/j.fishres.2023.106710_bib23) 2022; 55 Hau (10.1016/j.fishres.2023.106710_bib15) 2022 10.1016/j.fishres.2023.106710_bib28 Li (10.1016/j.fishres.2023.106710_bib26) 2020; 8 Ahmad (10.1016/j.fishres.2023.106710_bib1) 2020 Liu (10.1016/j.fishres.2023.106710_bib33) 2021; 12 Qi (10.1016/j.fishres.2023.106710_bib40) 2022; 194 Zhang (10.1016/j.fishres.2023.106710_bib65) 2021; 66 Yang (10.1016/j.fishres.2023.106710_bib60) 2021; 13 Unuma (10.1016/j.fishres.2023.106710_bib52) 2002 Shi (10.1016/j.fishres.2023.106710_bib44) 2021; 50 Hu (10.1016/j.fishres.2023.106710_bib18) 2021; 185 Wang (10.1016/j.fishres.2023.106710_bib54) 2017; 25 Xu (10.1016/j.fishres.2023.106710_bib58) 2018; 49 Gao (10.1016/j.fishres.2023.106710_bib10) 2022 Peng (10.1016/j.fishres.2023.106710_bib39) 2021 Ru (10.1016/j.fishres.2023.106710_bib42) 2018 Tian (10.1016/j.fishres.2023.106710_bib50) 2019; 157 Li (10.1016/j.fishres.2023.106710_bib25) 2021; 21 Fu (10.1016/j.fishres.2023.106710_bib9) 2020; 73 Ota (10.1016/j.fishres.2023.106710_bib38) 2017; Vol. 10133 Zhou (10.1016/j.fishres.2023.106710_bib69) 2020 Guo (10.1016/j.fishres.2023.106710_bib12) 2020 Redmon (10.1016/j.fishres.2023.106710_bib41) 2016 Liu (10.1016/j.fishres.2023.106710_bib30) 2022; 3 Han (10.1016/j.fishres.2023.106710_bib14) 2018; 153 Hoang Dinh (10.1016/j.fishres.2023.106710_bib16) 2019 Song (10.1016/j.fishres.2023.106710_bib47) 2020 Shi (10.1016/j.fishres.2023.106710_bib46) 2022 Beilei (10.1016/j.fishres.2023.106710_bib4) 2013 Wang (10.1016/j.fishres.2023.106710_bib53) 2021; 29 10.1016/j.fishres.2023.106710_bib48 Zeroual (10.1016/j.fishres.2023.106710_bib63) 2021 10.1016/j.fishres.2023.106710_bib49 Lei (10.1016/j.fishres.2023.106710_bib22) 2021 Zeng (10.1016/j.fishres.2023.106710_bib62) 2021; 100 Liu (10.1016/j.fishres.2023.106710_bib34) 2017 Mahmood (10.1016/j.fishres.2023.106710_bib36) 2020; 77 Li (10.1016/j.fishres.2023.106710_bib24) 2021 Mekhalfi (10.1016/j.fishres.2023.106710_bib37) 2021 Zhang (10.1016/j.fishres.2023.106710_bib66) 2022; 192 Wang (10.1016/j.fishres.2023.106710_bib56) 2022 Gray (10.1016/j.fishres.2023.106710_bib11) 2019; 10 Weihao (10.1016/j.fishres.2023.106710_bib57) 2020; 2 10.1016/j.fishres.2023.106710_bib61 Althnian (10.1016/j.fishres.2023.106710_bib3) 2021 Lei (10.1016/j.fishres.2023.106710_bib21) 2022 Lin (10.1016/j.fishres.2023.106710_bib29) 2019; 7 Lee (10.1016/j.fishres.2023.106710_bib20) 2020; 21 Ma (10.1016/j.fishres.2023.106710_bib35) 2022 Zhao (10.1016/j.fishres.2023.106710_bib67) 2021; 30 Darwish (10.1016/j.fishres.2023.106710_bib7) 2020; 53 10.1016/j.fishres.2023.106710_bib17 Shi (10.1016/j.fishres.2023.106710_bib45) 2020 10.1016/j.fishres.2023.106710_bib19 Xu (10.1016/j.fishres.2023.106710_bib59) 2022 10.1016/j.fishres.2023.106710_bib51 Zhan (10.1016/j.fishres.2023.106710_bib64) 2022; 483 Liu (10.1016/j.fishres.2023.106710_bib32) 2021; 51 Allken (10.1016/j.fishres.2023.106710_bib2) 2021; 78 Du (10.1016/j.fishres.2023.106710_bib8) 2022 Li (10.1016/j.fishres.2023.106710_bib27) 2022; 98 Setiawan (10.1016/j.fishres.2023.106710_bib43) 2017 Zhao (10.1016/j.fishres.2023.106710_bib68) 2021
References_xml	– reference: Tan, M., Pang, R., & Le, Q.V., 2020. EfficientDet: Scalable and Efficient Object Detection. In: In Proceedings of the IEEE conference on computer vision and pattern recognition, 10778–10787. – start-page: 192 year: 2022 ident: bib59 article-title: Detection and classification of tea buds based on deep learning publication-title: Comput. Electron. Agric. – start-page: 29 year: 2020 ident: bib45 article-title: Underwater targets detection and classification in complex scenes based on an improved YOLOv3 algorithm publication-title: J. Electron. Imaging – start-page: 1 year: 2020 end-page: 17 ident: bib47 article-title: Enhancement of underwater images with statistical model of background light and optimization of transmission map publication-title: IEEE Trans. Broadcast – start-page: 10 year: 2022 ident: bib8 article-title: Research on safety detection of transmission line disaster prevention based on improved lightweight convolutional neural network publication-title: MACHINES – volume: 185 year: 2021 ident: bib18 article-title: Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-V4 network publication-title: Comput. Electron. Agric. – volume: 30 start-page: 4719 year: 2021 end-page: 4734 ident: bib67 article-title: Composited fishnet: fish detection and species recognition from low-quality underwater videos publication-title: IEEE Trans. Image Process. – volume: 153 start-page: 248 year: 2018 end-page: 255 ident: bib14 article-title: Application driven key wavelengths mining method for aflatoxin detection using hyperspectral data publication-title: Comput. Electron. Agric. – reference: Li, X., & Liu, K. (2020). Restoration and Enhancement of Underwater Under-Exposure Images with Detail-Preserving. – volume: 55 start-page: 4077 year: 2022 end-page: 4116 ident: bib23 article-title: Recent advances of deep learning algorithms for aquacultural machine vision systems with emphasis on fish publication-title: Artif. Intell. Rev. – volume: 8 start-page: 18832 year: 2020 end-page: 18840 ident: bib26 article-title: Detection and analysis of behavior trajectory for sea cucumbers based on deep learning publication-title: IEEE Access – start-page: 12 year: 2022 ident: bib6 article-title: Plant disease recognition model based on improved YOLOv5 publication-title: Agron. -Basel – volume: 98 year: 2022 ident: bib27 article-title: Underwater scallop recognition algorithm using improved YOLOv5 publication-title: Aquacult. Eng. – reference: Yearbook, C.F.S. (2021): Fisheries Bureau of Ministry of Agriculture. – start-page: 68 year: 2022 ident: bib56 article-title: Recognition and counting of typical apple pests based on deep learning publication-title: Ecol. Inf. – reference: Karthi, M., Muthulakshmi, V., Priscilla, R., Praveen, P., & Vanisri, K., 2021. Evolution of YOLO-V5 Algorithm for Object Detection: Automated Detection of Library Books and Performace validation of Dataset. 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), 1–6. – start-page: 31 year: 2017 ident: bib34 article-title: Flame images for oxygen content prediction of combustion systems using DBN publication-title: Energy Fuels – volume: 483 start-page: 42 year: 2022 end-page: 58 ident: bib64 article-title: Evolutionary deep learning: a survey publication-title: Neurocomputing – volume: 51 start-page: 1085 year: 2021 end-page: 1093 ident: bib32 article-title: A novel sigmoid-function-based adaptive weighted particle swarm optimizer publication-title: IEEE Trans. Cybern. – volume: 2320 year: 2022 ident: bib55 article-title: Transmission line foreign body fault detection using multi-feature fusion based on modified YOLOv5 publication-title: J. Phys. Conf. Ser. – volume: 100 year: 2021 ident: bib62 article-title: Underwater target detection based on Faster R-CNN and adversarial occlusion network publication-title: Eng. Appl. Artif. Intell. – volume: 3 start-page: 20 year: 2022 ident: bib30 article-title: Continuous optical zoom microscope with extended depth of field and 3D reconstruction publication-title: PhotoniX – volume: 77 start-page: 1308 year: 2020 end-page: 1317 ident: bib36 article-title: Automatic detection of Western rock lobster using synthetic data publication-title: ICES J. Mar. Sci. – volume: 49 start-page: 73 year: 2018 end-page: 84 ident: bib58 article-title: Bioactive compounds and biological functions of sea cucumbers as potential functional foods publication-title: J. Funct. Foods – volume: Vol. 10133 year: 2017 ident: bib38 article-title: Evaluation of the sparse coding super-resolution method for improving image quality of up-sampled images in computed tomography publication-title: Proc. SPIE Int. Soc. Opt. Eng. – volume: 192 year: 2022 ident: bib66 article-title: Retinex-inspired color correction and detail preserved fusion for underwater image enhancement publication-title: Comput. Electron. Agric. – start-page: 182 year: 2021 ident: bib39 article-title: S-FPN: a shortcut feature pyramid network for sea cucumber detection in underwater images publication-title: Expert Syst. Appl. – volume: 29 start-page: 2681 year: 2021 end-page: 2711 ident: bib53 article-title: Intelligent fish farm—the future of aquaculture publication-title: Aquac. Int. – year: 2020 ident: bib1 article-title: Object detection through modified YOLO neural network publication-title: Sci. Program – volume: 25 start-page: 22490 year: 2017 end-page: 22498 ident: bib54 article-title: Feeble object detection of underwater images through LSR with delay loop publication-title: Opt. Express – year: 2021 ident: bib22 article-title: A novel intelligent underwater image enhancement method via color correction and contrast stretching publication-title: Microprocess. Microsyst. – reference: Hou, Q., Zhou, D., & Feng, J., 2021. Coordinate Attention for Efficient Mobile Network Design. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 13708–13717. – volume: 157 start-page: 417 year: 2019 end-page: 426 ident: bib50 article-title: Apple detection during different growth stages in orchards using the improved YOLO-V3 model publication-title: Comput. Electron. Agric. – year: 2022 ident: bib35 publication-title: Real. -Time Pers. Health Status Predict. Lithium-Ion.-. Batter. Using Deep Transf. Learn. – start-page: 1 year: 2021 end-page: 5 ident: bib37 article-title: Contrasting YOLOv5, transformer, and efficientdet detectors for crop circle detection in desert publication-title: IEEE Geosci. Remote Sens. Lett. – start-page: 22 year: 2018 ident: bib5 article-title: Multi-objective evolutionary algorithm for tuning the Type-2 inference engine on classification task publication-title: Soft Comput. – volume: 12 start-page: 1939 year: 2021 end-page: 1948 ident: bib33 article-title: A PSO-based deep learning approach to classifying patients from emergency departments publication-title: Int. J. Mach. Learn. Cybern. – volume: 66 start-page: 2087 year: 2021 end-page: 2104 ident: bib65 article-title: 3D reconstruction for motion blurred images using deep learning-based intelligent systems publication-title: Comput., Mater. Contin. – start-page: 8 year: 2021 ident: bib24 article-title: High-throughput plant phenotyping platform (HT3P) as a novel tool for estimating agronomic traits from the lab to the field publication-title: Front. Bioeng. Biotechnol. – volume: 10 start-page: 1490 year: 2019 end-page: 1500 ident: bib11 article-title: Drones and convolutional neural networks facilitate automated and accurate cetacean species identification and photogrammetry publication-title: Methods Ecol. Evol. – start-page: 37 year: 2018 ident: bib42 article-title: Development strategies for the sea cucumber industry in China publication-title: J. Oceanol. Limnol. – start-page: 19 year: 2022 ident: bib46 article-title: Design and control of underwater robot system for sea cucumber fishing publication-title: Int. J. Adv. Rob. Syst. – volume: 2 start-page: 43 year: 2020 end-page: 61 ident: bib57 article-title: Research progress on key technologies of aquaculture robots publication-title: Adv. Agric. Sci. – start-page: 11 year: 2021 ident: bib3 article-title: Impact of dataset size on classification performance: an empirical evaluation in the medical domain publication-title: Appl. Sci. -Basel – start-page: 779 year: 2016 end-page: 788 ident: bib41 article-title: You only look once: unified, real-time object detection publication-title: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. – start-page: 10 year: 2022 ident: bib21 article-title: Underwater target detection algorithm based on improved YOLOv5 publication-title: J. Mar. Sci. Eng. – start-page: 10 year: 2019 ident: bib16 article-title: Aquaculture breeding enhancement: maturation and spawning in sea cucumbers using a recombinant relaxin-like gonad-stimulating peptide publication-title: Front. Genet – start-page: 1 year: 2013 end-page: 4 ident: bib4 article-title: Underwater image color correct in extremely poor visibility publication-title: 2013 OCEANS - San. Diego – volume: 78 start-page: 3780 year: 2021 end-page: 3792 ident: bib2 article-title: A deep learning-based method to identify and count pelagic and mesopelagic fishes from trawl camera images publication-title: ICES J. Mar. Sci. – start-page: 197 year: 2022 ident: bib10 article-title: A novel apple fruit detection and counting methodology based on deep learning and trunk tracking in modern orchard publication-title: Comput. Electron. Agric. – volume: 73 start-page: 152 year: 2020 end-page: 162 ident: bib9 article-title: A multi-scale residual network for accelerated radial MR parameter mapping publication-title: Magn. Reson. Imaging – volume: 21 start-page: 2011 year: 2020 end-page: 2019 ident: bib20 article-title: Development of 3D reconstruction and object recognition model using video publication-title: J. Digit. Contents Soc. – volume: 13 start-page: 66 year: 2021 end-page: 90 ident: bib60 article-title: Deep learning for smart fish farming: applications, opportunities and challenges publication-title: Rev. Aquacult. – volume: 7 start-page: 148413 year: 2019 end-page: 148423 ident: bib29 article-title: Defect enhancement generative adversarial network for enlarging data set of microcrack defect publication-title: IEEE Access – volume: 21 start-page: 3374 year: 2021 ident: bib31 article-title: Real-time small drones detection based on pruned YOLOv4 publication-title: Sensors – start-page: 229 year: 2022 end-page: 235 ident: bib15 article-title: Using 3D shadows to detect object hiding attacks on autonomous vehicle perception publication-title: 2022 IEEE Secur. Priv. Workshops (SPW) – reference: Tian, Y., Lan, L., & Sun, L. (2020). A Review of Sonar Image Segmentation for Underwater Small Targets. – start-page: 1 year: 2021 end-page: 5 ident: bib13 article-title: Underwater image enhancement based on intrinsic images publication-title: OCEANS 2021: San. Diego – Porto – reference: Tan, M., Pang, R., & Le, Q. (2020). EfficientDet: Scalable and Efficient Object Detection. – volume: 194 year: 2022 ident: bib40 article-title: An improved YOLOv5 model based on visual attention mechanism: application to recognition of tomato virus disease publication-title: Comput. Electron. Agric. – start-page: 11 year: 2021 ident: bib68 article-title: Real-time detection of particleboard surface defects based on improved YOLOV5 target detection publication-title: Sci. Rep. – year: 2021 ident: bib63 article-title: Lightweight deep learning model to secure authentication in Mobile Cloud Computing publication-title: J. King Saud. Univ. - Comput. Inf. Sci. – reference: . – volume: 50 start-page: 13 year: 2021 end-page: 27 ident: bib44 article-title: Multi-object recognition method based on improved YOLOv2 model publication-title: Inf. Technol. Control – start-page: 179 year: 2020 ident: bib69 article-title: Real-time kiwifruit detection in orchard using deep learning on Android (TM) smartphones for yield estimation publication-title: Comput. Electron. Agric. – start-page: 701 year: 2020 end-page: 717 ident: bib12 article-title: Spiral generative network for image extrapolation publication-title: Proc. Eur. Conf. Comput. Vis. – volume: 53 start-page: 1767 year: 2020 end-page: 1812 ident: bib7 article-title: A survey of swarm and evolutionary computing approaches for deep learning publication-title: Artif. Intell. Rev. – start-page: 336 year: 2017 end-page: 341 ident: bib43 article-title: The use of scale invariant feature transform (SIFT) algorithms to identification garbage images based on product label publication-title: 2017 3rd Int. Conf. Sci. Inf. Technol. (ICSITech) – volume: 21 start-page: 136 year: 2021 ident: bib25 article-title: PSANet: pyramid splitting and aggregation network for 3D object detection in point cloud publication-title: Sensors – start-page: 115 year: 2002 end-page: 127 ident: bib52 article-title: Gonadal growth and its relationship to aquaculture in sea urchins. The sea urchin: from basic biology to aquaculture publication-title: Swets Zeit ., Lisse – volume: 21 start-page: 2011 year: 2020 ident: 10.1016/j.fishres.2023.106710_bib20 article-title: Development of 3D reconstruction and object recognition model using video publication-title: J. Digit. Contents Soc. doi: 10.9728/dcs.2020.21.11.2011 – volume: 78 start-page: 3780 year: 2021 ident: 10.1016/j.fishres.2023.106710_bib2 article-title: A deep learning-based method to identify and count pelagic and mesopelagic fishes from trawl camera images publication-title: ICES J. Mar. Sci. doi: 10.1093/icesjms/fsab227 – start-page: 1 year: 2013 ident: 10.1016/j.fishres.2023.106710_bib4 article-title: Underwater image color correct in extremely poor visibility publication-title: 2013 OCEANS - San. Diego – volume: 192 year: 2022 ident: 10.1016/j.fishres.2023.106710_bib66 article-title: Retinex-inspired color correction and detail preserved fusion for underwater image enhancement publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2021.106585 – volume: 21 start-page: 136 year: 2021 ident: 10.1016/j.fishres.2023.106710_bib25 article-title: PSANet: pyramid splitting and aggregation network for 3D object detection in point cloud publication-title: Sensors doi: 10.3390/s21010136 – start-page: 10 year: 2019 ident: 10.1016/j.fishres.2023.106710_bib16 article-title: Aquaculture breeding enhancement: maturation and spawning in sea cucumbers using a recombinant relaxin-like gonad-stimulating peptide publication-title: Front. Genet – start-page: 8 year: 2021 ident: 10.1016/j.fishres.2023.106710_bib24 article-title: High-throughput plant phenotyping platform (HT3P) as a novel tool for estimating agronomic traits from the lab to the field publication-title: Front. Bioeng. Biotechnol. – start-page: 31 year: 2017 ident: 10.1016/j.fishres.2023.106710_bib34 article-title: Flame images for oxygen content prediction of combustion systems using DBN publication-title: Energy Fuels – volume: 51 start-page: 1085 year: 2021 ident: 10.1016/j.fishres.2023.106710_bib32 article-title: A novel sigmoid-function-based adaptive weighted particle swarm optimizer publication-title: IEEE Trans. Cybern. doi: 10.1109/TCYB.2019.2925015 – volume: 98 year: 2022 ident: 10.1016/j.fishres.2023.106710_bib27 article-title: Underwater scallop recognition algorithm using improved YOLOv5 publication-title: Aquacult. Eng. doi: 10.1016/j.aquaeng.2022.102273 – start-page: 192 year: 2022 ident: 10.1016/j.fishres.2023.106710_bib59 article-title: Detection and classification of tea buds based on deep learning publication-title: Comput. Electron. Agric. – year: 2021 ident: 10.1016/j.fishres.2023.106710_bib63 article-title: Lightweight deep learning model to secure authentication in Mobile Cloud Computing publication-title: J. King Saud. Univ. - Comput. Inf. Sci. – volume: 153 start-page: 248 year: 2018 ident: 10.1016/j.fishres.2023.106710_bib14 article-title: Application driven key wavelengths mining method for aflatoxin detection using hyperspectral data publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2018.08.018 – year: 2022 ident: 10.1016/j.fishres.2023.106710_bib35 publication-title: Real. -Time Pers. Health Status Predict. Lithium-Ion.-. Batter. Using Deep Transf. Learn. – ident: 10.1016/j.fishres.2023.106710_bib17 doi: 10.1109/CVPR46437.2021.01350 – start-page: 19 year: 2022 ident: 10.1016/j.fishres.2023.106710_bib46 article-title: Design and control of underwater robot system for sea cucumber fishing publication-title: Int. J. Adv. Rob. Syst. – start-page: 22 year: 2018 ident: 10.1016/j.fishres.2023.106710_bib5 article-title: Multi-objective evolutionary algorithm for tuning the Type-2 inference engine on classification task publication-title: Soft Comput. – volume: 13 start-page: 66 year: 2021 ident: 10.1016/j.fishres.2023.106710_bib60 article-title: Deep learning for smart fish farming: applications, opportunities and challenges publication-title: Rev. Aquacult. doi: 10.1111/raq.12464 – start-page: 1 year: 2021 ident: 10.1016/j.fishres.2023.106710_bib37 article-title: Contrasting YOLOv5, transformer, and efficientdet detectors for crop circle detection in desert publication-title: IEEE Geosci. Remote Sens. Lett. – start-page: 37 year: 2018 ident: 10.1016/j.fishres.2023.106710_bib42 article-title: Development strategies for the sea cucumber industry in China publication-title: J. Oceanol. Limnol. – start-page: 779 year: 2016 ident: 10.1016/j.fishres.2023.106710_bib41 article-title: You only look once: unified, real-time object detection publication-title: Proc. IEEE Conf. Comput. Vis. Pattern Recognit. – ident: 10.1016/j.fishres.2023.106710_bib48 doi: 10.1109/CVPR42600.2020.01079 – volume: 21 start-page: 3374 year: 2021 ident: 10.1016/j.fishres.2023.106710_bib31 article-title: Real-time small drones detection based on pruned YOLOv4 publication-title: Sensors doi: 10.3390/s21103374 – start-page: 29 year: 2020 ident: 10.1016/j.fishres.2023.106710_bib45 article-title: Underwater targets detection and classification in complex scenes based on an improved YOLOv3 algorithm publication-title: J. Electron. Imaging – volume: 194 year: 2022 ident: 10.1016/j.fishres.2023.106710_bib40 article-title: An improved YOLOv5 model based on visual attention mechanism: application to recognition of tomato virus disease publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2022.106780 – start-page: 11 year: 2021 ident: 10.1016/j.fishres.2023.106710_bib3 article-title: Impact of dataset size on classification performance: an empirical evaluation in the medical domain publication-title: Appl. Sci. -Basel – start-page: 336 year: 2017 ident: 10.1016/j.fishres.2023.106710_bib43 article-title: The use of scale invariant feature transform (SIFT) algorithms to identification garbage images based on product label publication-title: 2017 3rd Int. Conf. Sci. Inf. Technol. (ICSITech) doi: 10.1109/ICSITech.2017.8257135 – volume: 29 start-page: 2681 year: 2021 ident: 10.1016/j.fishres.2023.106710_bib53 article-title: Intelligent fish farm—the future of aquaculture publication-title: Aquac. Int. doi: 10.1007/s10499-021-00773-8 – start-page: 68 year: 2022 ident: 10.1016/j.fishres.2023.106710_bib56 article-title: Recognition and counting of typical apple pests based on deep learning publication-title: Ecol. Inf. – start-page: 701 year: 2020 ident: 10.1016/j.fishres.2023.106710_bib12 article-title: Spiral generative network for image extrapolation publication-title: Proc. Eur. Conf. Comput. Vis. – year: 2021 ident: 10.1016/j.fishres.2023.106710_bib22 article-title: A novel intelligent underwater image enhancement method via color correction and contrast stretching publication-title: Microprocess. Microsyst. – volume: 50 start-page: 13 year: 2021 ident: 10.1016/j.fishres.2023.106710_bib44 article-title: Multi-object recognition method based on improved YOLOv2 model publication-title: Inf. Technol. Control doi: 10.5755/j01.itc.50.1.25094 – volume: 157 start-page: 417 year: 2019 ident: 10.1016/j.fishres.2023.106710_bib50 article-title: Apple detection during different growth stages in orchards using the improved YOLO-V3 model publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2019.01.012 – ident: 10.1016/j.fishres.2023.106710_bib19 doi: 10.1109/ICSES52305.2021.9633834 – volume: 12 start-page: 1939 year: 2021 ident: 10.1016/j.fishres.2023.106710_bib33 article-title: A PSO-based deep learning approach to classifying patients from emergency departments publication-title: Int. J. Mach. Learn. Cybern. doi: 10.1007/s13042-021-01285-w – start-page: 179 year: 2020 ident: 10.1016/j.fishres.2023.106710_bib69 article-title: Real-time kiwifruit detection in orchard using deep learning on Android (TM) smartphones for yield estimation publication-title: Comput. Electron. Agric. – volume: 73 start-page: 152 year: 2020 ident: 10.1016/j.fishres.2023.106710_bib9 article-title: A multi-scale residual network for accelerated radial MR parameter mapping publication-title: Magn. Reson. Imaging doi: 10.1016/j.mri.2020.08.013 – ident: 10.1016/j.fishres.2023.106710_bib28 doi: 10.21203/rs.2.24728/v1 – year: 2020 ident: 10.1016/j.fishres.2023.106710_bib1 article-title: Object detection through modified YOLO neural network publication-title: Sci. Program – volume: 49 start-page: 73 year: 2018 ident: 10.1016/j.fishres.2023.106710_bib58 article-title: Bioactive compounds and biological functions of sea cucumbers as potential functional foods publication-title: J. Funct. Foods doi: 10.1016/j.jff.2018.08.009 – volume: 100 year: 2021 ident: 10.1016/j.fishres.2023.106710_bib62 article-title: Underwater target detection based on Faster R-CNN and adversarial occlusion network publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2021.104190 – start-page: 1 year: 2021 ident: 10.1016/j.fishres.2023.106710_bib13 article-title: Underwater image enhancement based on intrinsic images publication-title: OCEANS 2021: San. Diego – Porto – start-page: 10 year: 2022 ident: 10.1016/j.fishres.2023.106710_bib8 article-title: Research on safety detection of transmission line disaster prevention based on improved lightweight convolutional neural network publication-title: MACHINES – ident: 10.1016/j.fishres.2023.106710_bib49 doi: 10.1109/CVPR42600.2020.01079 – volume: 3 start-page: 20 year: 2022 ident: 10.1016/j.fishres.2023.106710_bib30 article-title: Continuous optical zoom microscope with extended depth of field and 3D reconstruction publication-title: PhotoniX doi: 10.1186/s43074-022-00066-0 – volume: 483 start-page: 42 year: 2022 ident: 10.1016/j.fishres.2023.106710_bib64 article-title: Evolutionary deep learning: a survey publication-title: Neurocomputing doi: 10.1016/j.neucom.2022.01.099 – volume: 25 start-page: 22490 year: 2017 ident: 10.1016/j.fishres.2023.106710_bib54 article-title: Feeble object detection of underwater images through LSR with delay loop publication-title: Opt. Express doi: 10.1364/OE.25.022490 – volume: 10 start-page: 1490 year: 2019 ident: 10.1016/j.fishres.2023.106710_bib11 article-title: Drones and convolutional neural networks facilitate automated and accurate cetacean species identification and photogrammetry publication-title: Methods Ecol. Evol. doi: 10.1111/2041-210X.13246 – volume: 8 start-page: 18832 year: 2020 ident: 10.1016/j.fishres.2023.106710_bib26 article-title: Detection and analysis of behavior trajectory for sea cucumbers based on deep learning publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2962823 – volume: Vol. 10133 year: 2017 ident: 10.1016/j.fishres.2023.106710_bib38 article-title: Evaluation of the sparse coding super-resolution method for improving image quality of up-sampled images in computed tomography publication-title: Proc. SPIE Int. Soc. Opt. Eng. – start-page: 1 year: 2020 ident: 10.1016/j.fishres.2023.106710_bib47 article-title: Enhancement of underwater images with statistical model of background light and optimization of transmission map publication-title: IEEE Trans. Broadcast – start-page: 11 year: 2021 ident: 10.1016/j.fishres.2023.106710_bib68 article-title: Real-time detection of particleboard surface defects based on improved YOLOV5 target detection publication-title: Sci. Rep. – start-page: 115 year: 2002 ident: 10.1016/j.fishres.2023.106710_bib52 article-title: Gonadal growth and its relationship to aquaculture in sea urchins. The sea urchin: from basic biology to aquaculture publication-title: Swets Zeit ., Lisse – start-page: 12 year: 2022 ident: 10.1016/j.fishres.2023.106710_bib6 article-title: Plant disease recognition model based on improved YOLOv5 publication-title: Agron. -Basel – start-page: 229 year: 2022 ident: 10.1016/j.fishres.2023.106710_bib15 article-title: Using 3D shadows to detect object hiding attacks on autonomous vehicle perception publication-title: 2022 IEEE Secur. Priv. Workshops (SPW) doi: 10.1109/SPW54247.2022.9833890 – ident: 10.1016/j.fishres.2023.106710_bib61 – volume: 30 start-page: 4719 year: 2021 ident: 10.1016/j.fishres.2023.106710_bib67 article-title: Composited fishnet: fish detection and species recognition from low-quality underwater videos publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2021.3074738 – volume: 2 start-page: 43 year: 2020 ident: 10.1016/j.fishres.2023.106710_bib57 article-title: Research progress on key technologies of aquaculture robots publication-title: Adv. Agric. Sci. – start-page: 182 year: 2021 ident: 10.1016/j.fishres.2023.106710_bib39 article-title: S-FPN: a shortcut feature pyramid network for sea cucumber detection in underwater images publication-title: Expert Syst. Appl. – start-page: 197 year: 2022 ident: 10.1016/j.fishres.2023.106710_bib10 article-title: A novel apple fruit detection and counting methodology based on deep learning and trunk tracking in modern orchard publication-title: Comput. Electron. Agric. – volume: 2320 year: 2022 ident: 10.1016/j.fishres.2023.106710_bib55 article-title: Transmission line foreign body fault detection using multi-feature fusion based on modified YOLOv5 publication-title: J. Phys. Conf. Ser. doi: 10.1088/1742-6596/2320/1/012028 – volume: 55 start-page: 4077 year: 2022 ident: 10.1016/j.fishres.2023.106710_bib23 article-title: Recent advances of deep learning algorithms for aquacultural machine vision systems with emphasis on fish publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-021-10102-3 – start-page: 10 year: 2022 ident: 10.1016/j.fishres.2023.106710_bib21 article-title: Underwater target detection algorithm based on improved YOLOv5 publication-title: J. Mar. Sci. Eng. – volume: 66 start-page: 2087 year: 2021 ident: 10.1016/j.fishres.2023.106710_bib65 article-title: 3D reconstruction for motion blurred images using deep learning-based intelligent systems publication-title: Comput., Mater. Contin. – volume: 7 start-page: 148413 year: 2019 ident: 10.1016/j.fishres.2023.106710_bib29 article-title: Defect enhancement generative adversarial network for enlarging data set of microcrack defect publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2946062 – volume: 185 year: 2021 ident: 10.1016/j.fishres.2023.106710_bib18 article-title: Real-time detection of uneaten feed pellets in underwater images for aquaculture using an improved YOLO-V4 network publication-title: Comput. Electron. Agric. doi: 10.1016/j.compag.2021.106135 – volume: 53 start-page: 1767 year: 2020 ident: 10.1016/j.fishres.2023.106710_bib7 article-title: A survey of swarm and evolutionary computing approaches for deep learning publication-title: Artif. Intell. Rev. doi: 10.1007/s10462-019-09719-2 – volume: 77 start-page: 1308 year: 2020 ident: 10.1016/j.fishres.2023.106710_bib36 article-title: Automatic detection of Western rock lobster using synthetic data publication-title: ICES J. Mar. Sci. doi: 10.1093/icesjms/fsz223 – ident: 10.1016/j.fishres.2023.106710_bib51 doi: 10.1145/3415048.3416098
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Snippet	Underwater object recognition is a prerequisite for the realization of automated seafood harvesting. To solve the problems of low recognition accuracy and poor...
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SubjectTerms	algorithms aquaculture Aquatic animal Artificial Intelligence (AI) automation Bidirectional Feature Pyramid Network (BiFPN) Coordinate Attention (CA) data collection Deep learning Holothuroidea Object recognition Sea cucumber seafoods YOLOv5
Title	SO-YOLOv5: Small object recognition algorithm for sea cucumber in complex seabed environment
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