Research on bridge defect detection based on deep learning
Despite significant advancements in object detection technology in recent years,multi-object detection in complex environments still faces numerous challenges.To address these issues,this study improved the faster RCNN model.Researchers opted for ResNet101 as the feature extraction network,replacing the traditional VGG16,to alleviate problems caused by information decay due to increased network depth and to enhance the efficiency of feature learning.Additionally,a multi-scale fusion module was introduced in the study,which can more effectively handle targets of different sizes,thereby enhancing detection performance.Experimental results show that the improved faster RCNN model performs excellently in bridge defect detection tasks,achieving an accuracy rate of 91.4%and mean average precision of 90.6%.It has significant practical application value for timely identification and repair of structural issues in bridges,providing strong technical support for bridge maintenance and management work.
万方数据
维普
