Opto-Electronic Engineering, Volume. 51, Issue 11, 240212-1(2024)
DES-YOLO: a more accurate object detection method
Figures & Tables(17)
Fig. 6. Comparison of detection accuracy of different network models
Table 1. Experimental environment and configuration
View tableView in ArticleTable 1. Experimental environment and configuration
Type Configuration GPU NVIDIA GeFore RTX4090 CPU 13th Gen Intel(R) Core(TM) i7-13620H CUDA 11.7 Deep learning framework Pytorch Python 3.12
Table 2. Remote sensing target detection data set
View tableView in ArticleTable 2. Remote sensing target detection data set
Precision Recall mAP@0.5 mAP@0.5:0.95 YOLOv5s 0.937 0.899 0.929 0.562 YOLOv5s+CBAM 0.939 0.884 0.923 0.511 YOLOv5s+CA 0.922 0.886 0.931 0.551 YOLOv5s+DA 0.945 0.898 0.942 0.561
Table 3. Textile defect detection data set
View tableView in ArticleTable 3. Textile defect detection data set
Precision Recall mAP@0.5 mAP@0.5:0.95 YOLOv5s 0.350 0.322 0.276 0.118 YOLOv5s+CBAM 0.382 0.290 0.282 0.141 YOLOv5s+CA 0.237 0.296 0.219 0.086 YOLOv5s+DA 0.350 0.342 0.285 0.121
Table 4. Effect comparison of remote sensing target detection loss function
View tableView in ArticleTable 4. Effect comparison of remote sensing target detection loss function
Precision Recall mAP@0.5 mAP@0.5:0.95 YOLOv5s+DA+CIoU 0.945 0.898 0.942 0.561 YOLOv5s+DA+EIoU 0.936 0.914 0.944 0.573 YOLOv5s+DA+SIoU 0.933 0.922 0.935 0.571 YOLOv5s+DA+WIoU 0.848 0.842 0.884 0.506
Table 5. Effect comparison of textile defect detection loss function
View tableView in ArticleTable 5. Effect comparison of textile defect detection loss function
Precision Recall mAP@0.5 mAP@0.5:0.95 YOLOv5s+DA+CIoU 0.350 0.342 0.285 0.121 YOLOv5s+DA+EIoU 0.392 0.315 0.281 0.130 YOLOv5s+DA+SIoU 0.382 0.272 0.283 0.144 YOLOv5s+DA+WIoU 0.357 0.292 0.258 0.103
Table 6. Remote sensing target detection
View tableView in ArticleTable 6. Remote sensing target detection
Params/M Precision Recall mAP@0.5 mAP@0.5:0.95 Ship YOLOv5s+DA 8.1 0.936 0.914 0.944 0.573 0.973 YOLOv5s+DA+STD 8.7 0.963 0.903 0.943 0.613 0.98
Table 7. Textile defect detection
View tableView in ArticleTable 7. Textile defect detection
Params/M Precision Recall mAP@0.5 mAP@0.5:0.95 Knot head YOLOv5s+DA 8.1 0.392 0.315 0.281 0.13 0.303 YOLOv5s+DA+STD 8.7 0.454 0.258 0.293 0.14 0.311
Table 8. Results of ablation experiments
View tableView in ArticleTable 8. Results of ablation experiments
Params/M Precision Recall mAP@0.5 mAP@0.5:0.95 YOLOv5s 7.0 0.937 0.899 0.929 0.562 YOLOv5s+DA 8.1 0.945 0.898 0.942 0.561 YOLOv5s+DA+EIoU 8.1 0.936 0.914 0.944 0.573 YOLOv5s+DA+EIoU+STD 8.7 0.963 0.903 0.943 0.613
Table 9. Results of ablation experiments
View tableView in ArticleTable 9. Results of ablation experiments
Params/M Precision Recall mAP@0.5 mAP@0.5:0.95 YOLOv5s 7.1 0.350 0.322 0.276 0.118 YOLOv5s+DA 8.1 0.350 0.342 0.285 0.121 YOLOv5s+DA+EIoU 8.1 0.392 0.315 0.281 0.130 YOLOv5s+DA+EIoU+STD 8.7 0.454 0.258 0.293 0.140
Table 10. Results of comparison experiments
View tableView in ArticleTable 10. Results of comparison experiments
Params/M Precision Recall mAP@0.5 mAP@0.5:0.95 GFLOPs/G Faster-RCNN 41.1 0.861 0.957 0.901 0.559 33.2 YOLOv3-tiny 8.6 0.952 0.860 0.929 0.544 12.9 YOLOv3 61.5 0.956 0.918 0.952 0.602 155.4 YOLOv5s 7.0 0.937 0.899 0.929 0.562 15.8 YOLOv5m 20.9 0.864 0.832 0.888 0.523 48.0 YOLOv7-tiny 6.0 0.786 0.631 0.768 0.386 13.3 YOLOv8s 11.1 0.906 0.867 0.932 0.601 28.5 DES-YOLO 8.7 0.963 0.903 0.943 0.613 27.9
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Huawei Zheng, Fei Wang, Jianbang Gao. DES-YOLO: a more accurate object detection method[J]. Opto-Electronic Engineering, 2024, 51(11): 240212-1
Paper Information
Category: Article
Received: Sep. 9, 2024
Accepted: Oct. 11, 2024
Published Online: Jan. 24, 2025
The Author Email: Wang Fei (王飞)
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