基于全局信息感知的轻量级螺纹钢表面缺陷检测算法
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肖剑,杨小苑,何昕泽,陈林,胡欣
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Lightweight rebar surface defect detection algorithm based on global information perception
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Jian XIAO,Xiaoyuan YANG,Xinze HE,Lin CHEN,Xin HU
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| 表 6 先进主流模型对比实验 |
| Tab.6 Comparative experiments of advanced mainstream models |
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| 模型 | P/% | R/% | mAP@0.5/% | mAP@0.5-0.95/% | Np/M | FLOPs/G | FPS/(帧·s−1) | | Faster R-CNN | 82.1 | 72.9 | 82.7 | 44.6 | 137.1 | 370.2 | 6 | | SSD | 80.6 | 80.7 | 85.0 | 49.3 | 41.1 | 145.3 | 41 | | YOLOv3-Tiny | 80.8 | 79.9 | 86.3 | 50.9 | 12.1 | 18.9 | 78 | | YOLOv4-Tiny | 84.3 | 81.0 | 88.4 | 52.1 | 5.9 | 16.1 | 113 | | YOLOv5n | 85.5 | 81.2 | 89.4 | 52.5 | 2.4 | 7.1 | 107 | | YOLOv6n | 81.6 | 80.6 | 87.2 | 51.3 | 4.2 | 11.2 | 130 | | YOLOv7-Tiny | 85.4 | 81.2 | 89.3 | 54.7 | 6.1 | 13.1 | 109 | | YOLOX-Tiny | 86.2 | 82.5 | 90.3 | 56.1 | 5.1 | 6.5 | 122 | | YOLOv8n | 88.3 | 83.1 | 90.2 | 55.9 | 3.0 | 8.1 | 133 | | YOLOv9n | 85.5 | 83.3 | 90.7 | 56.5 | 2.3 | 8.4 | 135 | | YOLOv10n | 85.6 | 82.7 | 90.5 | 55.9 | 2.7 | 8.2 | 131 | | YOLOv11n | 86.1 | 83.4 | 90.9 | 55.8 | 2.6 | 6.3 | 136 | | YOLOv8-VSC | 88.6 | 88.8 | 92.7 | 56.3 | 2.0 | 6.0 | 152 | | LTSCD-YOLO | 89.2 | 87.8 | 91.9 | 56.5 | 2.4 | 9.8 | 144 | | S-YOLO | 89.8 | 87.9 | 93.2 | 57.9 | 2.6 | 8.4 | 127 | | 本研究模型 | 90.2 | 89.0 | 94.5 | 58.8 | 1.4 | 5.3 | 156 |
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