Improved Small Object Detection Algorithm CRL-YOLOv5

Detecting small objects in images poses significant challenges due to their limited pixel representation and the difficulty in extracting sufficient features, often leading to missed or false detections. To address these challenges and enhance detection accuracy, this paper presents an improved smal...

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Vydáno v:	Sensors (Basel, Switzerland) Ročník 24; číslo 19; s. 6437
Hlavní autoři:	Wang, Zhiyuan, Men, Shujun, Bai, Yuntian, Yuan, Yutong, Wang, Jiamin, Wang, Kanglei, Zhang, Lei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 01.10.2024 MDPI
Témata:	Accuracy Algorithms attention mechanisms contextual information digital images Medical imaging equipment Remote sensing Semantics small object detection spatial resolution Telecommunication systems Telematics YOLOv5 small object detection spatial resolution YOLOv5 contextual information attention mechanisms digital images
ISSN:	1424-8220, 1424-8220
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Abstract	Detecting small objects in images poses significant challenges due to their limited pixel representation and the difficulty in extracting sufficient features, often leading to missed or false detections. To address these challenges and enhance detection accuracy, this paper presents an improved small object detection algorithm, CRL-YOLOv5. The proposed approach integrates the Convolutional Block Attention Module (CBAM) attention mechanism into the C3 module of the backbone network, which enhances the localization accuracy of small objects. Additionally, the Receptive Field Block (RFB) module is introduced to expand the model’s receptive field, thereby fully leveraging contextual information. Furthermore, the network architecture is restructured to include an additional detection layer specifically for small objects, allowing for deeper feature extraction from shallow layers. When tested on the VisDrone2019 small object dataset, CRL-YOLOv5 achieved an mAP50 of 39.2%, representing a 5.4% improvement over the original YOLOv5, effectively boosting the detection precision for small objects in images.
AbstractList	Detecting small objects in images poses significant challenges due to their limited pixel representation and the difficulty in extracting sufficient features, often leading to missed or false detections. To address these challenges and enhance detection accuracy, this paper presents an improved small object detection algorithm, CRL-YOLOv5. The proposed approach integrates the Convolutional Block Attention Module (CBAM) attention mechanism into the C3 module of the backbone network, which enhances the localization accuracy of small objects. Additionally, the Receptive Field Block (RFB) module is introduced to expand the model's receptive field, thereby fully leveraging contextual information. Furthermore, the network architecture is restructured to include an additional detection layer specifically for small objects, allowing for deeper feature extraction from shallow layers. When tested on the VisDrone2019 small object dataset, CRL-YOLOv5 achieved an mAP50 of 39.2%, representing a 5.4% improvement over the original YOLOv5, effectively boosting the detection precision for small objects in images. Detecting small objects in images poses significant challenges due to their limited pixel representation and the difficulty in extracting sufficient features, often leading to missed or false detections. To address these challenges and enhance detection accuracy, this paper presents an improved small object detection algorithm, CRL-YOLOv5. The proposed approach integrates the Convolutional Block Attention Module (CBAM) attention mechanism into the C3 module of the backbone network, which enhances the localization accuracy of small objects. Additionally, the Receptive Field Block (RFB) module is introduced to expand the model's receptive field, thereby fully leveraging contextual information. Furthermore, the network architecture is restructured to include an additional detection layer specifically for small objects, allowing for deeper feature extraction from shallow layers. When tested on the VisDrone2019 small object dataset, CRL-YOLOv5 achieved an mAP50 of 39.2%, representing a 5.4% improvement over the original YOLOv5, effectively boosting the detection precision for small objects in images.Detecting small objects in images poses significant challenges due to their limited pixel representation and the difficulty in extracting sufficient features, often leading to missed or false detections. To address these challenges and enhance detection accuracy, this paper presents an improved small object detection algorithm, CRL-YOLOv5. The proposed approach integrates the Convolutional Block Attention Module (CBAM) attention mechanism into the C3 module of the backbone network, which enhances the localization accuracy of small objects. Additionally, the Receptive Field Block (RFB) module is introduced to expand the model's receptive field, thereby fully leveraging contextual information. Furthermore, the network architecture is restructured to include an additional detection layer specifically for small objects, allowing for deeper feature extraction from shallow layers. When tested on the VisDrone2019 small object dataset, CRL-YOLOv5 achieved an mAP50 of 39.2%, representing a 5.4% improvement over the original YOLOv5, effectively boosting the detection precision for small objects in images.
Audience	Academic
Author	Wang, Jiamin Wang, Kanglei Bai, Yuntian Men, Shujun Wang, Zhiyuan Yuan, Yutong Zhang, Lei
AuthorAffiliation	2 Silesian College of Intelligent Science and Engineering, Yanshan University, Qinhuangdao 066004, China; baiyuntian186@stumail.ysu.edu.cn 1 School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; zhiyuanwang@stumail.ysu.edu.cn (Z.W.); menshujun@stumail.ysu.edu.cn (S.M.); ysuyyt@stumail.ysu.edu.cn (Y.Y.); wjm@stumail.ysu.edu.cn (J.W.); wangkl@stumail.ysu.edu.cn (K.W.)
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SubjectTerms	Accuracy Algorithms attention mechanisms contextual information digital images Medical imaging equipment Remote sensing Semantics small object detection spatial resolution Telecommunication systems Telematics YOLOv5
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Title	Improved Small Object Detection Algorithm CRL-YOLOv5
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