摘要
Abstract
A modified algorithm for steel surface defect detection,named ADP-YOLOv8,is proposed to address the issues of uneven scale of steel surface defects,poor multi-scale feature processing ability of existing detection algorithms,and the need to improve accuracy.Firstly,an adaptive weighted downsampling(ADSConv)module is proposed,which enhances the detector's adaptability to different types of defects by weighting and combining different downsampling feature maps.Then,by improving the C2F module in the feature extraction network,the extraction of features from the scalable receptive field at the higher level of the network is strengthened.Finally,the introduction of the programmable gradient information(PGI)module gradually integrates features of different scales through its multi-level auxiliary information components.The average accuracy of the present method is 79.3％,which is 3.5％higher than the benchmark model.The detection speed is 163.2 frame/s.Comparing with the other mainstream object detection algorithms,the improved detector has more advantages in performance,demonstrating a good balance in detection accuracy,speed and model volume.关键词
表面缺陷检测/自适应权重/感受野/可编程梯度信息Key words
surface defect detection/adaptive weight/receptive field/programmable gradient information分类
信息技术与安全科学
