多特征融合的YOLOv4-tiny带钢表面缺陷检测方法研究
STUDY ON SURFACE DEFECT DETECTION METHOD OF YOLOV4-TINY STRIP BY MULTI-FEATURE FUSION
李锦达 1汤勃 1孙伟 1孔建益 1林中康1
作者信息
- 1. 武汉科技大学机械自动化学院 湖北 武汉 430081
- 折叠
摘要
微小表面缺陷自动识别是带钢生产过程中的研究难点之一.为了提高带钢表面缺陷检测的准确性,提出一种多特征融合的YOLOv4-tiny深度学习方法.引入Inception结构与多尺度信息.提取原始图片的方向梯度直方图特征(HOG),并与主干网络所提取的高层特征相融合,作为特征金字塔结构的输入.实验结果表明,该算法在测试集中带钢表面缺陷mAP达到93.99%,相比原网络提高了13.57 百分点,网络参数量相比于原网络减少约21 万,网络检测精度有较大的提升.
Abstract
Automatic identification of small surface defects is one of the difficulties in strip production.In order to improve the accuracy of surface defect detection of strip steel,a multi-feature fusion YOLOv4-tiny deep learning method is proposed.The Inception structure and multi-scale information were introduced.The orientation gradient histogram feature(HOG)of the original image was extracted and fused with the high-level features extracted from the backbone network as the input of the feature pyramid structure.The experimental results show that the mAP of surface defects of strip steel in the test concentration is 93.99%,which is 13.57 percentage points higher than that of the YOLOv4-tiny network.The number of network parameters was reduced by about 210 000 compared with that of the YOLOv4-tiny network,and the network detection accuracy is greatly improved.
关键词
带钢/表面缺陷检测/特征融合/YOLOv4-tiny/深度学习Key words
Strip steel/Surface defect detection/Feature fusion/YOLOv4-tiny/Deep learning引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据