计算机应用与软件2024，Vol.41Issue(12)：208-213,254,7.DOI:10.3969/j.issn.1000-386x.2024.12.030
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
摘要
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分类
信息技术与安全科学引用本文复制引用
李锦达,汤勃,孙伟,孔建益,林中康..多特征融合的YOLOv4-tiny带钢表面缺陷检测方法研究[J].计算机应用与软件,2024,41(12):208-213,254,7.基金项目
国家自然科学基金项目(51874217). （51874217）
