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Figure 1.
Images of the gap between PSDs and metro doors.
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Figure 2.
Schematic diagram of data collection and sample images.
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Figure 3.
Network architecture[37] of YOLOv3.
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Figure 4.
Building blocks of ShuffleNetV1 and ShuffleNetV2.
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Figure 5.
FPS vs mAP on the present dataset. FPS is measured on the RTX 3090 GPU.
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Foreign object categories Training and validation Testing Total Rope 472 123 595 Cord 363 96 509 Wig 19 5 24 School bag 70 19 89 Plastic bag 507 121 628 Box 72 15 87 Shoulder bag 292 66 358 Wallet 549 136 690 Cell phone 491 134 625 Bottle 722 206 928 Umbrella 79 15 94 Person 87 10 97 Others 53 12 65 Normal 510 124 634 Cardboard 362 100 512 Total 4748 1187 5935 Table 1.
The statistics of the current dataset.
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Algorithms Backbone Size mAP@0.5 FPS Model size (MB) SSD VGG 300 × 300 0.859 126 97.7 YOLOv3 Darknet-53 640 × 640 0.889 213 117 YOLOv3 Darknet-53-SPP 640 × 640 0.879 208 119 YOLOv4 CSPDarknet53 with Mish activation 640 × 480 0.869 92 245 YOLOv4 Leaky-CSPDarknet53 with Leaky activation 640 × 480 0.876 93 245 YOLOv4 SAM-Leaky-CSPDarknet53 with Leaky activation-SAM 640 × 480 0.876 86 250 YOLOv4 Mish-CSPDarknet53 with Mish activation 640 × 480 0.867 92 245 YOLOv4 SAM-Mish-CSPDarknet53 with Mish activation-SAM 640 × 480 0.874 86 250 YOLOv5-m CSPDarknet-SPP 640 × 640 0.884 233 40.6 YOLOv5-l CSPDarknet-SPP 640 × 640 0.884 154 89.5 YOLOv5-x** CSPDarknet-SPP 640 × 640 0.894 72 167 YOLOX-m Modified CSP in YOLOv5 640 × 640 0.865 149 194 YOLOX-l Modified CSP in YOLOv5 640 × 640 0.868 105 364 YOLOX-x Modified CSP in YOLOv5 640 × 640 0.854 57 757 YOLOX-DarkNet53 Darknet-53 640 × 640 0.848 92 487 PPYOLOv1 ResNet18-vd 512 × 512 0.831 75 49.5 PPYOLOv1 ResNet50-vd-dcn 608 × 608 0.843 47 178 PPYOLOv2 ResNet50-vd-dcn 640 × 640 0.849 42 207 PPYOLOv2 ResNet101-vd-dcn 640 × 640 0.855 37 279 SSD MobileNetV1 300 × 300 0.819 157 22 SSDLite MobileNetV1 300 × 300 0.865 159 23 SSDLite MobileNetV3-Small 320 × 320 0.849 140 5.1 SSDLite MobileNetV3-Large 320 × 320 0.857 143 11 SSDLite GhostNet 320 × 320 0.868 142 23 YOLOv3 MobileNetV1 608 × 608 0.847 83 93 YOLOv3 MobileNetV3 608 × 608 0.854 80 89 YOLOv3-Tiny* Darknet-53 640 × 640 0.854 1667 16.6 YOLOv4-Tiny CSPDarknet-53 640 × 480 0.831 549 22.5 YOLOv5-s*** CSPDarknet-SPP 640 × 640 0.88 588 13.7 YOLOv5-Lite ShuffleNetv2 640 × 640 0.871 1250 3.3 YOLOX-s Modified CSP in YOLOv5 640 × 640 0.848 282 69 YOLOX-Tiny Modified CSP in YOLOv5 640 × 640 0.854 560 39 YOLOX-Nano Modified CSP in YOLOv5 640 × 640 0.84 804 7.3 PPYOLOv1 MobileNetV3-Small 320 × 320 0.856 147 9.9 PPYOLOv1 MobileNetV3-Large 320 × 320 0.865 148 18 PPYOLOv1 PPYOLO-Tiny 320 × 320 0.818 190 3.95 * Fastest, ** highest mAP, *** best one in comparison. Table 2.
Experimental results on the present dataset.
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Class YOLOv5-x YOLOv5-s P R F1 mAP@0.5 mAP@0.5:0.95 P R F1 mAP@0.5 mAP@0.5:0.95 Rope 0.883 0.764 0.819 0.839 0.515 0.865 0.729 0.791 0.836 0.499 Cord 0.826 0.693 0.754 0.822 0.41 0.757 0.646 0.697 0.75 0.384 Wig 0.779 0.8 0.789 0.938 0.521 0.789 0.8 0.794 0.84 0.426 School bag 0.899 0.737 0.81 0.84 0.516 0.901 0.737 0.811 0.825 0.461 Plastic bag 0.972 0.967 0.969 0.979 0.695 0.967 0.977 0.972 0.97 0.68 Box 0.975 1 0.987 0.995 0.811 0.976 1 0.988 0.995 0.809 Shoulder bag 0.955 0.985 0.97 0.984 0.72 0.969 0.985 0.977 0.983 0.703 Wallet 0.874 0.887 0.88 0.912 0.452 0.903 0.924 0.913 0.906 0.479 Cell phone 0.989 0.963 0.976 0.987 0.561 0.962 0.957 0.959 0.962 0.556 Bottle 0.994 0.995 0.994 0.993 0.642 0.994 0.995 0.994 0.993 0.639 Umbrella 0.865 1 0.928 0.946 0.64 0.86 0.933 0.895 0.899 0.586 Person 0.896 1 0.945 0.995 0.942 0.964 1 0.982 0.995 0.902 Others 0.581 0.579 0.58 0.405 0.22 0.666 0.666 0.666 0.477 0.208 Normal 0.821 0.628 0.712 0.794 0.399 0.834 0.649 0.73 0.811 0.415 Cardboard 0.971 0.96 0.965 0.982 0.576 0.96 0.98 0.97 0.958 0.576 All 0.885 0.864 0.874 0.894 0.575 0.891 0.865 0.878 0.88 0.555 P: precision, R: recall, mAP@0.5:0.95: average mAP over different IoU thresholds, from 0.5 to 0.95, step 0.05 (0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95). Table 3.
PR values and F1 scores of YOLOv5-x, -s.
Figures
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Tables
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