四川轻化工大学学报（自然科学版）2025，Vol.38Issue(1)：47-56,10.DOI:10.11863/j.suse.2025.01.06
基于YOLOv7-NBC的钢材表面缺陷检测算法研究
Research on Steel Surface Defect Detection Algorithm Based on YOLOv7-NBC
摘要
Abstract
To solve the problem that the difference between small size defects on steel surface and complex background is too low,leading to poor detection effect,a steel surface defect detection algorithm based on YOLOv7-NBC is proposed,with NBC representing the introduced NWD metric,BiFormer and Cascade Fusion Network(CFNet)respectively.The dynamic sparse attention module is introduced in the 24th layer of the backbone network of YOLOv7 algorithm to improve the feature learning ability of the algorithm.By seeking the optimal ratio of IoU metric to NWD metric,better loss is obtained to reduce the bias sensitivity of steel surface defect location and improve the detection performance of the algorithm.The cascade fusion network structure is introduced into the backbone network to reduce the number of algorithm parameters.The improved YOLOv7-NBC algorithm is applied to the optimized NEU-DET dataset for ablation and comparison experiments.The experimental results show that the detection accuracy of the YOLOv7-NBC algorithm is significantly improved,with mAP reaching 85.4%,an increase of 4.6%.The calculation amount of YOLOv7-NBC algorithm is reduced by 52.1%,the FPS reaches 70,and the industrial detection efficiency is improved.YOLOv7-NBC algorithm has higher detection accuracy,stronger generalization ability,and lower error and missed detection rate.关键词
复杂背景/小尺寸缺陷/缺陷检测/YOLOv7/动态稀疏注意力模块/级联融合网络结构Key words
complex background/small size defects/defect detection/YOLOv7/BiFormer/cascade fusion network分类
信息技术与安全科学引用本文复制引用
李淇,石艳,林峰,郝琪..基于YOLOv7-NBC的钢材表面缺陷检测算法研究[J].四川轻化工大学学报（自然科学版）,2025,38(1):47-56,10.基金项目
四川省科技计划重点研发项目(2022YFG0068) （2022YFG0068）
四川省大学生创新创业训练项目(CX2023074) （CX2023074）
