轻量化YOLOv5s网络车底危险物识别算法
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金鑫,庄建军,徐子恒
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Lightweight YOLOv5s network-based algorithm for identifying hazardous objects under vehicles
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Xin JIN,Jian-jun ZHUANG,Zi-heng XU
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| 表 3 SG-YOLOv5s网络模型消融实验结果分析 |
| Tab.3 Analysis of ablative experimental results for SG-YOLOv5s network model |
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| 模型 | P/% | R/% | Par/MB | Me/MB | FPS | mAP_0.5/% | | ①YOLOv5s | 96.59 | 94.08 | 7.03 | 26.81 | 50.39 | 96.37 | | ②YOLOV5s+Mixup | 97.60 | 94.31 | 7.03 | 26.81 | 49.22 | 96.49 | | ③YOLOv5s+Backbone+Mixup | 96.17 | 94.50 | 4.15 | 15.83 | 46.37 | 96.49 | | ④YOLOv5s+Neck+Mixup | 96.99 | 96.96 | 4.90 | 18.68 | 45.33 | 97.00 | | ⑤Backbone+Neck+Mixup+CIoU | 96.92 | 97.02 | 2.02 | 7.70 | 47.04 | 97.19 | | ⑥Backbone+Neck+Mixup+SIoU | 96.80 | 97.96 | 2.02 | 7.70 | 47.39 | 97.63 |
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