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8 June 2023 Lightweight target detection method based on improved YOLOv5
Jianxin Feng, Minghao Zhou, Enguang Hao, Chengsheng Pan
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Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127072Z (2023) https://doi.org/10.1117/12.2680941
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China
Abstract
Target detection is one of the hot research issues in the field of computer vision in recent years. Large scale convolution networks can effectively improve the accuracy of target detection, but they are not suitable for application scenarios with limited computing and storage capacity. In order to solve the above problems, this paper proposes a lightweight target detection network based on improved YOLOv5 - CS-YOLOv5 network. This network uses a lightweight network ShuffleNetv2 to reconstruct backbone, effectively reducing the network complexity, and adds a CA attention module to the feature extraction network to enhance the network feature expression ability, and solves the problem of neuron deactivation through H-Swish activation function. The final model size was compressed to 1.64MB, and the mAP50 on the COCO test set reached 47.6%, reducing the model size by 13.7% and improving the detection accuracy by 1.9% compared with the original YOLOv5 model.
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Jianxin Feng, Minghao Zhou, Enguang Hao, and Chengsheng Pan "Lightweight target detection method based on improved YOLOv5", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127072Z (8 June 2023); https://doi.org/10.1117/12.2680941
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KEYWORDS
Object detection
Detection and tracking algorithms
Target detection
Convolution
Feature extraction
Deep learning
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