Remote Sensing Technology and Application, Volume. 39, Issue 3, 547(2024)
Research on Lightweight Network for Rapid Detection of Remote Sensing Image Targets based on YOLO
Figures & Tables(9)
Table 1.
Ablation experiments of the modified module View tableView in ArticleTable 1.
Ablation experiments of the modified module Mobilenetv3tiny Ghost Alpha-IoU VFL Weights Parameters mAP FPS - - - - 34 M 8.94 M 92.23% 48.02f/s √ - - - 18 M 4.75 M 90.88% 49.46f/s - √ - - 27 M 7.26 M 93.02% 54.73f/s - - √ - 34 M 8.94 M 94.91% 46.39f/s - - - √ 34 M 8.94 M 93.94% 48.36f/s - - √ √ 34 M 8.94 M 95.08% 46.98f/s √ √ √ √ 11 M 3.07 M 92.99% 57.49f/s
Table 2.
Experimental results of DIOR dataset View tableView in ArticleTable 2.
Experimental results of DIOR dataset Method Parameters/M Weights/M mAP/% FPS/(f/s) Faster RCNN 138.10 522 57.35 10.42 AOPG(DIOR⁃R) - - 64.41 - Li’s (DIOR⁃R)- - 66.71 - LO-Det 6.93 27 65.85 60.03 ASSD 43.70 - 71.13 21 Yao’s - - 75.80 - YOLOv5 7.27 28 80.96 - YOLOX-tiny 5.04 19 71.66 - YOLOX-s 8.94 34 82.23 47.99 MGV-YOLO 3.07 11 77.44 60.89
Table 3.
AP values of the three algorithms under different categories of DIOR View tableView in ArticleTable 3.
AP values of the three algorithms under different categories of DIOR Method C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 mAP YOLOX-tiny 0.68 0.84 0.79 0.86 0.42 0.78 0.71 0.86 0.66 0.80 0.78 0.61 0.59 0.79 0.69 0.61 0.87 0.67 0.40 0.82 0.71 YOLOX-s 0.96 0.88 0.96 0.83 0.48 0.78 0.78 0.94 0.79 0.83 0.90 0.69 0.71 0.96 0.96 0.87 0.95 0.65 0.62 0.92 0.82 MGV-YOLO 0.97 0.77 0.95 0.73 0.49 0.92 0.57 0.89 0.75 0.75 0.79 0.70 0.65 0.89 0.96 0.72 0.93 0.49 0.66 0.92 0.77
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Wei WANG, Yong CHENG, Yuke ZHOU, Wenjie ZHANG, Jun WANG, Jiaxin HE, Yakang GU. Research on Lightweight Network for Rapid Detection of Remote Sensing Image Targets based on YOLO[J]. Remote Sensing Technology and Application, 2024, 39(3): 547
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Received: Dec. 11, 2022
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Published Online: Dec. 9, 2024
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