基于改进YOLOv3的印刷电路板缺陷检测算法

卞佰成,陈田,吴入军,刘军

Improved YOLOv3-based defect detection algorithm for printed circuit board

Bai-cheng BIAN,Tian CHEN,Ru-jun WU,Jun LIU

表 6 PCB缺陷数据集上不同算法的参数对比和检测精度测试结果

Tab.6 Parameter comparison and detection accuracy test results of different algorithms on PCB defect dataset

网络模型 主干网络 NP/106 AP，AR/% AP0.5/% AP0.75/% GFLOPs Faster R-CNN[12] VGG-16 — — 58.57 — — Faster R-CNN[12] ResNet-101 — — 94.27 — — FPN[12] ResNet-101 — — 92.23 — — Faster R-CNN(fine-tuned)[12] ResNet-101 — — 96.44 — — TDD-Net[12] ResNet-101 — — 98.90 — — YOLOv3[12] DarkNet53 — — 81.42 — — YOLOv3-ultralytics DarkNet53 62.999 58.78，33.05 96.71 64.64 66.5 YOLOv5m CSP-DarkNet53 21.077 61.17，34.27 98.43 68.21 32.3 YOLOv5l CSP-DarkNet53 46.658 64.88，35.77 98.95 75.45 73.2 本文算法 ResNeSt50 35.227 64.53，35.49 98.42 76.23 45.9