Real-time detection of YOLOv5 guns based on lightweight

Jiacheng Zeng; Zhilin Zhu

doi:10.1117/12.2685079

19 October 2023 Real-time detection of YOLOv5 guns based on lightweight

Jiacheng Zeng, Zhilin Zhu

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127094O (2023) https://doi.org/10.1117/12.2685079
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Aiming at the problems of large number of parameters, large amount of calculation, and few types of recognition of existing gun detection models deployed to mobile devices or embedded devices, the lightweight and high-precision optimization of YOLOv5s object detection algorithm is studied. The lightweight network MobbileNetv3 is introduced on the YOLOv5s architecture to realize the lightweight detection network. At the same time, the attention mechanism CBAM is added to enhance the feature extraction ability and the knowledge distillation algorithm is introduced to improve the accuracy, so as to improve the accuracy of object detection. The experimental results show that on the Open Images gun dataset, the number of parameters and calculations of the optimized YOLOv5s are reduced by 47.8% and 61.6%, respectively, and the detection accuracy remains unchanged.

Citation Download Citation

Jiacheng Zeng and Zhilin Zhu "Real-time detection of YOLOv5 guns based on lightweight", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127094O (19 October 2023); https://doi.org/10.1117/12.2685079

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KEYWORDS

Object detection

Convolution

Detection and tracking algorithms

Performance modeling

Education and training

Ablation

Firearms

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