Improved target detection algorithm based on YOLOv5s
Authors: 
Chunxiao Han, Boyang Chen, Han WuAuthors Info & Claims
Chunxiao Han, Boyang Chen, Han WuAuthors Info & ClaimsPages 15 - 20
Abstract
Target detection is a research hotspot in the field of computer vision and machine learning, and the deep learning model greatly improves the efficiency of target detection and classification by obtaining feature information and classification through multi-layer neural networks, which is the mainstream algorithm for target detection at present. In this paper, YOLOv5s target detection algorithm is improved, and the SIoU loss function is chosen to replace the CIoU loss function used in the original model, while the SimAM attention mechanism is added to the Neck module of the YOLOv5s model, and it is proved through experiments that the improved model improves the average precision ([email protected]) by 0.2% over the original model, and the recall rate (Recall) is improved by 0.8%. The faster convergence and improved precision of the model further improves the performance of the model.
References
[1]
Xingyao Yu, Yuhan Jia, and Hongzheng Ni, Vehicle Detection for UAV Images based on Fine-grained YOLOv3[J]. International Core Journal of Engineering,2022,8(2):188-197.
[2]
Lin C, Jhang J. Intelligent Traffic-Monitoring System Based on YOLO and Convolutional Fuzzy Neural Networks[J]. IEEE Access,2022,10:14120-14133.
[3]
Yunqiu Lu. Integrated target tracking algorithm based on deep learning [D]. Xi'an: Xi'an University of Electronic Science and Technology, 2019.
[4]
Jingjing Q, Yunhong X. Combined continuous frame difference with background difference method for moving object detection[J]. Acta photonica sinica, 2014, 43(7): 0710002.
[5]
Y. Le,Z. Zhao. Multi-motion target detection and segmentation based on background difference method [J]. China Journal of Construction Machinery, 2020, 18(04):305-309.
[6]
Yang Lu. A classroom headcounting algorithm based on human contour features[J]. Journal of Metrology, 2021, 42(2): 178-183.
[7]
Yongliang Xie, Liurong Hong, Fangzhen Ge, and Qiu Doli. A review of motion target detection algorithms in video scenes[J]. Journal of Luoyang Normal College, 2015, 34(08): 35-38.
[8]
Girshick R, Donahue J, Darrell T, Rich feature hierarchies for accurate object detection and semantic segmentation[C]. Proceedings of the IEEE conference on computer vision and pattern recognition. Columbus, OH, USA: IEEE, 2014:580-587.
[9]
Girshick R. Fast R-CNN[C]. Proceedings of the IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015: 1440-1448.
[10]
Ren S, He K, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[11]
Redmon J, Divvala S, Girshick R, You only look once: Unified, real-time object detection[C]. Proceedings of the IEEE conference on computer vision and pattern recognition. Las Vegas, NY, USA: IEEE.
[12]
Liu W, Anguelov D, Erhan D, SSD: single shot multibox detector[C]. Proceedings of European Conference on Computer Vision. 2016: 21-37.
[13]
Zhang T, Xu C, Yang M H. Multi-task correlation particle filter for robust object tracking[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017:4335-434
[14]
Lin T Y, Dollár P, Girshick R, Feature pyramid networks for object detection[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 2117-2125.
Index Terms
- Improved target detection algorithm based on YOLOv5s
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Published In
June 2024
116 pages
ISBN:9798400718045
DOI:10.1145/3674700
Copyright © 2024 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 30 August 2024
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ICCCV 2024
ICCCV 2024: 2024 the 6th International Conference on Control and Computer Vision
June 13 - 15, 2024
Tianjin, China
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