湖南大学学报（自然科学版）2024，Vol.51Issue(12)：67-77,11.DOI:10.16339/j.cnki.hdxbzkb.2024252
基于改进YOLOv8的热轧带钢表面缺陷检测方法
Surface Defect Detection Method for Hot-rolled Strip Steel Based on Improved YOLOv8
摘要
Abstract
A object detection algorithm based on improved YOLOv8s is proposed to address the issues of low accuracy and low efficiency in surface defect detection of hot-rolled strip steel.Firstly,an SPPD module based on feature map secondary stitching and incorporating GAM is proposed,which enhances the model's multi-scale information fusion ability.Secondly,a feature extraction module DCN-block that integrates deformable convolution is proposed to increase the receptive field of the model and extract complete defect information.Finally,the C2f module in the feature fusion network is replaced with a BoT(bottleneck transformer)structure,and the multi-head self-attention mechanism in the Transformer is fused with convolution to enhance the model's global position information perception ability.The experimental results show that the proposed algorithm achieves mean average precision(mAP)of 80.5%on the NEU-DET dataset,which is five percentage points higher than the original YOLOv8 algorithm.At the same time,the detection speed reaches 83 frames per second,meeting the requirements of real-time detection.关键词
热轧带钢/表面缺陷/目标检测/深度学习Key words
hot-rolled strip steel/surface defect/object detection/deep learning分类
信息技术与安全科学引用本文复制引用
肖科,杨昕宇,韩彦峰,宋斌..基于改进YOLOv8的热轧带钢表面缺陷检测方法[J].湖南大学学报（自然科学版）,2024,51(12):67-77,11.基金项目
国家重点研发计划资助项目(2022YFB4702201),National Key Reaearch and Development Program of China(2022YFB4702201) （2022YFB4702201）
国家自然科学基金资助项目(52375039),National Natural Science Foundation of China(52375039) （52375039）
