计算机工程与应用2024，Vol.60Issue(24)：211-221,11.DOI:10.3778/j.issn.1002-8331.2406-0223
YOLOv8-FD:YOLOv8改进的钢板表面缺陷检测方法
YOLOv8-FD:YOLOv8 Improved Method for Detecting Surface Defects on Steel Plates
摘要
Abstract
Steel surface defect detection is an important challenge in the field of defect detection,there is still a serious sit-uation of leakage and misdetection,and its detection accuracy is directly related to product quality,and may even jeopar-dize life safety.At the same time,the application of this technology to the actual production needs to consider resource saving and cost reduction.To solve these problems,a method based on the lightweight detection model YOLOv8-FD is intro-duced.Three major strategies are used:(1)A feature extraction module is added to C2f to better understand and utilize the input image information,and a DCN is introduced to enhance the feature extraction capability and improve the perfor-mance of the target detection;(2)A DUFPN is proposed to fuse the contextual features more efficiently,which drastically reduces the number of parameters and computation to achieve the lightweighting of the network;(3)W-CIOU is intro-duced as a bounding box loss function to better measure the similarity between targets,accelerate convergence,and improve target detection accuracy.The experimental results show that the model improves mAP by 5 percentage points,R by 3.3 percentage points,the amount of parameters by 27%,and the amount of computation by 35%compared with the baseline.In addition,the algorithm is confirmed to have good robustness through validation on the APSPC and VOC2007 datasets.关键词
缺陷检测/钢板缺陷/YOLOv8/轻量化Key words
defect detection/steel plate defects/YOLOv8/lightweighting分类
信息技术与安全科学引用本文复制引用
马磊,李晔,王宇翔..YOLOv8-FD:YOLOv8改进的钢板表面缺陷检测方法[J].计算机工程与应用,2024,60(24):211-221,11.基金项目
山西省自然科学基金(20210302123222) （20210302123222）
山西省重点研发计划项目(202302010101006). （202302010101006）
