摘要
Abstract
In this paper,a defect detection method for the inner wall of engine steel tube based on deep learning technology is pro-posed,which aims to improve the accuracy and efficiency of detection.Traditional detection methods rely on manual observation,which is easily affected by subjective and environmental factors,and has limited accuracy.In view of these limitations,an improved network struc-ture is designed,which combines the ConvNeXt V2 backbone network and the Adaptive Feature Pooling Network(AFPN)neck to en-hance the ability of feature extraction and multi-scale object detection.As the backbone network,ConvNeXt V2 adopts innovative convo-lution structure and Global Response Normalization(GRN)technology,which effectively improves the feature expression ability and re-duces the computational complexity.GRN technology enhances the contrast and selectivity between channels through aggregation and normalization operations,prevents feature collapse,and improves feature diversity.In the neck of AFPN,the adaptive feature pooling technology is used to integrate the feature maps of different scales,which enhances the model's ability to detect multi-scale targets,espe-cially for the identification of defects in complex scenes.The improved network structure improves the sensitivity of the model to different types of defect features through feature fusion technology.The results show that the use of the improved network mAP50%is increased by 8.9%.关键词
缺陷检测/目标检测/特征提取/多尺度特征融合Key words
Defect detection/Object detection/Feature extraction/Multi-scale feature fusion分类
信息技术与安全科学
