Automatic object detection for behavioural research using YOLOv8
Observational studies of human behaviour often require the annotation of objects in video recordings. Automatic object detection has been facilitated strongly by the development of YOLO (‘you only look once’) and particularly by YOLOv8 from Ultralytics, which is easy to use. The present study examin...
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| Published in: | Behavior research methods Vol. 56; no. 7; pp. 7307 - 7330 |
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| Language: | English |
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01.10.2024
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| ISSN: | 1554-3528, 1554-3528 |
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| Abstract | Observational studies of human behaviour often require the annotation of objects in video recordings. Automatic object detection has been facilitated strongly by the development of YOLO (‘you only look once’) and particularly by YOLOv8 from Ultralytics, which is easy to use. The present study examines the conditions required for accurate object detection with YOLOv8. The results show almost perfect object detection even when the model was trained on a small dataset (100 to 350 images). The detector, however, does not extrapolate well to the same object in other backgrounds. By training the detector on images from a variety of backgrounds, excellent object detection can be restored. YOLOv8 could be a game changer for behavioural research that requires object annotation in video recordings. |
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| AbstractList | Observational studies of human behaviour often require the annotation of objects in video recordings. Automatic object detection has been facilitated strongly by the development of YOLO (‘you only look once’) and particularly by YOLOv8 from Ultralytics, which is easy to use. The present study examines the conditions required for accurate object detection with YOLOv8. The results show almost perfect object detection even when the model was trained on a small dataset (100 to 350 images). The detector, however, does not extrapolate well to the same object in other backgrounds. By training the detector on images from a variety of backgrounds, excellent object detection can be restored. YOLOv8 could be a game changer for behavioural research that requires object annotation in video recordings. Observational studies of human behaviour often require the annotation of objects in video recordings. Automatic object detection has been facilitated strongly by the development of YOLO ('you only look once') and particularly by YOLOv8 from Ultralytics, which is easy to use. The present study examines the conditions required for accurate object detection with YOLOv8. The results show almost perfect object detection even when the model was trained on a small dataset (100 to 350 images). The detector, however, does not extrapolate well to the same object in other backgrounds. By training the detector on images from a variety of backgrounds, excellent object detection can be restored. YOLOv8 could be a game changer for behavioural research that requires object annotation in video recordings.Observational studies of human behaviour often require the annotation of objects in video recordings. Automatic object detection has been facilitated strongly by the development of YOLO ('you only look once') and particularly by YOLOv8 from Ultralytics, which is easy to use. The present study examines the conditions required for accurate object detection with YOLOv8. The results show almost perfect object detection even when the model was trained on a small dataset (100 to 350 images). The detector, however, does not extrapolate well to the same object in other backgrounds. By training the detector on images from a variety of backgrounds, excellent object detection can be restored. YOLOv8 could be a game changer for behavioural research that requires object annotation in video recordings. |
| Author | Hermens, Frouke |
| Author_xml | – sequence: 1 givenname: Frouke surname: Hermens fullname: Hermens, Frouke email: frouke.hermens@ou.nl organization: Open University of the Netherlands |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38750389$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1007/978-3-319-10602-1_48 10.14569/IJACSA.2021.0121013 10.1002/alr.21053 10.3390/s21217422 10.1109/ACCESS.2021.3094201 10.1016/j.jss.2022.05.024 10.1371/journal.pone.0121792 10.1007/978-1-4842-4470-8_7 10.1016/j.cmpb.2021.106251 10.3389/fpsyg.2012.00445 10.1109/IAEAC.2018.8577214 10.31219/osf.io/m6jb2 10.1109/RIVF48685.2020.9140740 10.1109/CVPR.2015.7299023 10.1109/TIV.2022.3165353 10.1016/j.cognition.2015.08.005 10.1068/p2935 10.1016/j.jss.2014.04.032 10.1016/j.vlsi.2019.07.005 10.1007/978-3-642-15711-0_37 10.1007/s00221-004-1862-9 10.1109/ICARM52023.2021.9536075 10.1109/EMBC.2017.8037183 10.1109/CVPR.2009.5206848 10.3390/s20041074 10.1016/j.cviu.2022.103508 10.16910/jemr.6.4.4 10.1109/ACCESS.2020.3046515 10.3390/rs12152501 10.1016/j.procs.2022.01.135 10.1016/j.actpsy.2014.01.009 |
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| Keywords | Surgical tool tracking Automatic object detection YOLO Behavioural analysis |
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Retrieved from https://github.com/ultralytics/ultralytics LiGJiZQuXZhouRCaoDCross-domain object detection for autonomous driving: A stepwise domain adaptative YOLO approachIEEE Transactions on Intelligent Vehicles20227360361510.1109/TIV.2022.3165353 Krizhevsky, A., Hinton, G., et al. (2009). Learning multiple layers of features from tiny images. Yu, Z., Huang, H., Chen, W., Su, Y., Liu, Y., & Wang, X. (2022). YOLO-facev2: A scale and occlusion aware face detector. arXiv:2208.02019 FengXJiangYYangXDuMLiXComputer vision algorithms and hardware implementations: A surveyIntegration20196930932010.1016/j.vlsi.2019.07.005 PhamM-TCourtraiLFriguetCLefèvreSBaussardAYOLO-fine: One-stage detector of small objects under various backgrounds in remote sensing imagesRemote Sensing20201215250110.3390/rs12152501 KuhnGTeszkaRTenawNKingstoneADon’t be fooled! attentional responses to social cues in a face-to-face and video magic trick reveals greater top-down control for overt than covert attentionCognition201614613614210.1016/j.cognition.2015.08.00526407341 Gil, A. M., Birdi, S., Kishibe, T., & Grantcharov, T. P. (2022). Eye tracking use in surgical research: A systematic review. Journal of Surgical Research,279, 774–787. CohenRGRosenbaumDAWhere grasps are made reveals how grasps are planned: generation and recall of motor plansExperimental Brain Research200415748649510.1007/s00221-004-1862-915071711 MinaeeSBoykovYPorikliFPlazaAKehtarnavazNTerzopoulosDImage segmentation using deep learning: A surveyIEEE Transactions on Pattern Analysis and Machine Intelligence202144735233542 ChoiJChoSChungJWKimNVideo recognition of simple mastoidectomy using convolutional neural networks: Detection and segmentation of surgical tools and anatomical regionsComputer Methods and Programs in Biomedicine202120810.1016/j.cmpb.2021.10625134271262 Hermens, F. (2017). The influence of social stigmas on observers’ eye movements. Journal of Articles in Support of the Null Hypothesis, 14 (1). KnudsenBHenningAWunschKWeigeltMAscherslebenGThe end-state comfort effect in 3-to 8-year-old children in two object manipulation tasksFrontiers in Psychology2012344510.3389/fpsyg.2012.00445231127863482869 Tien, T., Pucher, P. H., Sodergren, M. H., Sriskandarajah, K., Yang, G.-Z., & Darzi, A. (2014). Eye tracking for skills assessment and training: A systematic review. Journal of Surgical Research,191(1), 169–178. Ahmidi, N., Hager, G. D., Ishii, L., Fichtinger, G., Gallia, G. L., & Ishii, M. (2010). Surgical task and skill classification from eye tracking and tool motion in minimally invasive surgery. In: Medical image computing and computer-assisted intervention–MICCAI 2010: 13th international conference, Beijing, China, September 20–24, 2010, Proceedings, part III 13 (pp. 295–302). Wang, Y., Sun, Q., Sun, G., Gu, L., & Liu, Z. (2021). Object detection of surgical instruments based on yolov4. In: 2021 6th IEEE International Conference on Advanced Robotics and Mechatronics (ICARM) (pp. 578–581). LandMMennieNRustedJThe roles of vision and eye movements in the control of activities of daily livingPerception199928111311132810.1068/p293510755142 Ahmidi, N., Ishii, M., Fichtinger, G., Gallia, G. L., & Hager, G. D. (2012). An objective and automated method for assessing surgical skill in endoscopic sinus surgery using eye-tracking and tool-motion data. In: International forum of allergy & rhinology (vol. 2, pp. 507–515). ChenFWangXZhaoYLvSNiuXVisual object tracking: A surveyComputer Vision and Image Understanding202222210.1016/j.cviu.2022.103508 DewiCChenR-CLiuY-TJiangXHartomoKDYOLO V4 for advanced traffic sign recognition with synthetic training data generated by various GANIEEE Access20219972289724210.1109/ACCESS.2021.3094201 Choi, B., Jo, K., Choi, S., & Choi, J. (2017). Surgical-tools detection based on convolutional neural network in laparoscopic robot-assisted surgery. In: 2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 1756–1759). Li, G., Song, Z., & Fu, Q. (2018). A new method of image detection for small datasets under the framework of YOLO network. In: 2018 IEEE 3rd advanced information technology, electronic and automation control conference (IAEAC) (pp. 1031–1035). Wada, K. (2018). labelme: Image polygonal annotation with python. https://github.com/wkentaro/labelme. GitHub. ChenWYuCTuCLyuZTangJOuSXueZA survey on hand pose estimation with wearable sensors and computer-vision-based methodsSensors2020204107410.3390/s20041074320791247071082 LiYLiSDuHChenLZhangDLiYYOLO-ACN: Focusing on small target and occluded object detectionIEEE Access2020822728822730310.1109/ACCESS.2020.3046515 Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition (pp. 248–255). Himabindu, D. D., & Kumar, S. P. (2021). A survey on computer vision architectures for large scale image classification using deep learning. International Journal of Advanced Computer Science and Applications, 12(10). Bisong, E., & Bisong, E. (2019). Google colaboratory. Building machine learning and deep learning models on google cloud platform: a comprehensive guide for beginners, 59–64. Chung, Q. M., Le, T. D., Dang, T. V., Vo, N. D., Nguyen, T. V., & Nguyen, K. (2020). Data augmentation analysis in vehicle detection from aerial videos. In: 2020 RIVF international conference on computing and communication technologies (RIVF) (pp. 1–3). JiangPErguDLiuFCaiYMaBA review of yolo algorithm developmentsProcedia Computer Science20221991066107310.1016/j.procs.2022.01.135 Yang, L., Luo, P., Change Loy, C., & Tang, X. (2015). A large-scale car dataset for fine-grained categorization and verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3973–3981). Gregory, N. J., López, B., Graham, G., Marshman, P., Bate, S., & Kargas, N. (2015). Reduced gaze following and attention to heads when viewing a “live"’social scene. PLoS One,10(4) 2420_CR29 2420_CR1 2420_CR3 F Hermens (2420_CR18) 2014; 148 2420_CR2 X He (2420_CR15) 2021; 21 2420_CR27 2420_CR6 2420_CR8 W Chen (2420_CR5) 2020; 20 RG Cohen (2420_CR9) 2004; 157 2420_CR21 2420_CR23 F Chen (2420_CR4) 2022; 222 S Minaee (2420_CR30) 2021; 44 2420_CR17 G Li (2420_CR26) 2022; 7 2420_CR19 C Dewi (2420_CR11) 2021; 9 2420_CR14 2420_CR36 2420_CR13 2420_CR35 2420_CR16 J Choi (2420_CR7) 2021; 208 Y Li (2420_CR28) 2020; 8 B Knudsen (2420_CR22) 2012; 3 X Feng (2420_CR12) 2019; 69 M-T Pham (2420_CR31) 2020; 12 2420_CR10 2420_CR32 2420_CR34 2420_CR33 G Kuhn (2420_CR24) 2016; 146 M Land (2420_CR25) 1999; 28 P Jiang (2420_CR20) 2022; 199 |
| References_xml | – reference: HeXChengRZhengZWangZSmall object detection in traffic scenes based on YOLO-MXANetSensors20212121742210.3390/s21217422347707268588269 – reference: Himabindu, D. D., & Kumar, S. P. (2021). A survey on computer vision architectures for large scale image classification using deep learning. International Journal of Advanced Computer Science and Applications, 12(10). – reference: Tien, T., Pucher, P. H., Sodergren, M. H., Sriskandarajah, K., Yang, G.-Z., & Darzi, A. (2014). Eye tracking for skills assessment and training: A systematic review. Journal of Surgical Research,191(1), 169–178. – reference: Yang, L., Luo, P., Change Loy, C., & Tang, X. (2015). A large-scale car dataset for fine-grained categorization and verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3973–3981). – reference: Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., . . . Zitnick, C. L. (2014). Microsoft COCO: Common objects in context. In: Computer vision–ECCV 2014: 13th European conference, Zurich, Switzerland, September 6-12, 2014, proceedings, part v 13 (pp. 740–755). – reference: HermensFKralDRosenbaumDALimits of end-state planningActa Psychologica201414814816210.1016/j.actpsy.2014.01.00924531145 – reference: LiYLiSDuHChenLZhangDLiYYOLO-ACN: Focusing on small target and occluded object detectionIEEE Access2020822728822730310.1109/ACCESS.2020.3046515 – reference: MinaeeSBoykovYPorikliFPlazaAKehtarnavazNTerzopoulosDImage segmentation using deep learning: A surveyIEEE Transactions on Pattern Analysis and Machine Intelligence202144735233542 – reference: Gil, A. M., Birdi, S., Kishibe, T., & Grantcharov, T. P. (2022). Eye tracking use in surgical research: A systematic review. Journal of Surgical Research,279, 774–787. – reference: Krizhevsky, A., Hinton, G., et al. (2009). Learning multiple layers of features from tiny images. – reference: Ahmidi, N., Ishii, M., Fichtinger, G., Gallia, G. L., & Hager, G. D. (2012). An objective and automated method for assessing surgical skill in endoscopic sinus surgery using eye-tracking and tool-motion data. In: International forum of allergy & rhinology (vol. 2, pp. 507–515). – reference: JiangPErguDLiuFCaiYMaBA review of yolo algorithm developmentsProcedia Computer Science20221991066107310.1016/j.procs.2022.01.135 – reference: PhamM-TCourtraiLFriguetCLefèvreSBaussardAYOLO-fine: One-stage detector of small objects under various backgrounds in remote sensing imagesRemote Sensing20201215250110.3390/rs12152501 – reference: Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition (pp. 248–255). – reference: FengXJiangYYangXDuMLiXComputer vision algorithms and hardware implementations: A surveyIntegration20196930932010.1016/j.vlsi.2019.07.005 – reference: Hermens, F., Flin, R., & Ahmed, I. (2013). Eye movements in surgery: A literature review. Journal of Eye Movement Research, 6(4). – reference: Li, G., Song, Z., & Fu, Q. (2018). A new method of image detection for small datasets under the framework of YOLO network. In: 2018 IEEE 3rd advanced information technology, electronic and automation control conference (IAEAC) (pp. 1031–1035). – reference: Choi, B., Jo, K., Choi, S., & Choi, J. (2017). Surgical-tools detection based on convolutional neural network in laparoscopic robot-assisted surgery. In: 2017 39th annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 1756–1759). – reference: Gregory, N. 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| Title | Automatic object detection for behavioural research using YOLOv8 |
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