Lightweight remote sensing ship detection algorithm based on YOLOv5s

Table 1. Backbone network table
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Table 1. Backbone network table
层步长重复次数输出通道数
ShuffleNet v2 Block 2 1 116
ShuffleNet v2 Block 1 3 116
ShuffleNet v2 Block 2 1 232
ShuffleNet v2 Block 1 7 232
ShuffleNet v2 Block 2 1 464
ShuffleNet v2 Block 1 3 464

Table 2. Design of ablation experiments
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Table 2. Design of ablation experiments
方法 CSL ShuffleNet v2 Block Filter DCN
A √
B √ √
C √ √ √
D √ √ √
E √ √ √ √

Table 3. Results of ablation experiment
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Table 3. Results of ablation experiment
方法 CSL ShuffleNet v2 Block Filter DCN mAP/% FLOPs/B Params/M
A √ 91.60 16.5 5.39
B √ √ 89.70 1.85 2.04
C √ √ √ 90.80 1.87 2.05
D √ √ √ 90.30 1.94 2.11
E √ √ √ √ 92.90 1.96 2.12

Table 4. YOLOv8 model performance comparison
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Table 4. YOLOv8 model performance comparison
Model mAP/% FLOPs/B Params/M
YOLOv8s 95.32 3.5 4.5
YOLOv8m 95.65 9.6 10.4
YOLOv8l 96.42 31.1 17.5

Table 5. Comparison of results of different algorithms on the HRSC2016 dataset
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Table 5. Comparison of results of different algorithms on the HRSC2016 dataset
Model mAP/% FLOPs/B Params/M
Baseline 91.60 16.5 5.39
ShuffNetv2-YOLOv5s 90.14 11.4 5.54
MobileNetv3-YOLOv5s 87.06 6.30 3.54
Faster RCNN 87.60
STYOLO^[16] 94.33 6.20 2.68
文献[20] 90.04
LR-YOLO 92.90 1.96 2.12

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Haochen WANG, Yuelan XIN, Jiang GUO, Qingqing WANG. Lightweight remote sensing ship detection algorithm based on YOLOv5s[J]. Chinese Journal of Ship Research, 2024, 19(5): 200

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