Research on Bridge Defects Detection Based on Faster RCNN
Although target detection technology has made significant progress in recent years,multi-target detection in complex environments still faces many challenges.To address the problems encountered by the Faster RCNN model in bridge detection,three aspects are proposed for improvement:by adopting ResNet101 as the feature extraction network instead of the traditional VGG16,it is to alleviate the problem of attenuation of in-formation transfer due to the increase in the depth of the network,and to improve the efficiency of feature learn-ing;by introducing a recursive feature pyramid structure,different scales of targets can be dealt with more effi-ciently,thus to enhance the detection performance;by embedding the attention mechanism in the model,it fur-ther strengthens the model's ability to recognize key regions and reduces the influence of background noise,so that it can focus more on target features.As a result of the improvements,the accuracy of the model was in-creased to 92.5%,with an average accuracy of 91.5%.
万方数据
维普
