Opto-Electronic Engineering, Volume. 51, Issue 1, 230284-1(2024)
Lightweight YOLOv5 sonar image object detection algorithm and implementation based on ZYNQ
Figures & Tables(15)
Fig. 1. Lightweight sonar image YOLOv5 object detection network model
Table 1. Running results of different equipments
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Equipment FLOPs/G Memory footprint/MB Frame rate/FPS Power/W Frame rate/power/(FPS/W) GPU 6.0 1401 36.4 250 0.146 CPU 6.0 2103 8.8 65 0.135 ZYNQ7020 5.8 90 0.6 3.7 0.162
Table 2. Comparative experiments
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Algorithm Map50 Params(M) YOLOv5s 0.912 7.2 EfficientDet 0.834 3.9 YOLOv7 0.890 37 Faster-RCNN 0.896 25.6 SSD 0.912 51 This paper 0.933 3.2
Table 3. Ablation experiments
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Algorithm Precision Recall Map50 Map50-95 Params(M) YOLOv5s 0.896 0.905 0.912 0.685 7.2 YOLOv5s+SimAM 0.892 0.911 0.922 0.700 7.0 YOLOv5s+CBAM 0.896 0.927 0.918 0.697 7.1 YOLOv5s+SimAM+CBAM 0.892 0.912 0.925 0.701 7.1 YOLOv5s+Focal-CIOU 0.899 0.891 0.930 0.700 7.2 YOLOv5s+DWConv 0.880 0.911 0.919 0.694 3.6 YOLOv5s+GSConv 0.897 0.906 0.923 0.699 6.2 This paper 0.907 0.894 0.933 0.712 3.2
Table 4. Comparison experiment of classic datasets
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Algorithm Precision Recall Map50 Map50-95 YOLOv5s 0.678 0.53 0.583 0.353 This paper 0.661 0.519 0.59 0.339
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Dongdong Zhao, Dunhan Xie, Peng Chen, Ronghua Liang, Yi Shen, Xinxin Guo. Lightweight YOLOv5 sonar image object detection algorithm and implementation based on ZYNQ[J]. Opto-Electronic Engineering, 2024, 51(1): 230284-1
Paper Information
Category: Article
Received: Nov. 21, 2023
Accepted: Jan. 10, 2024
Published Online: Apr. 19, 2024
The Author Email: Peng Chen (陈朋)
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