Semantic Segmentation of Concrete Bridge Multiple Defects Combined with Attention Mechanism
Aiming at the problems in semantic segmentation of concrete bridge defects images,such as insufficient precision,and limited computing power of mobile devices,the concrete bridge defects data sets(including spallation,cracks and exposed reinforcement)is established.In the constructed multiple semantic segmentation models,deep convolutional network and lightweight convolutional network are used as the backbone feature extraction network,and different attention mechanism modules are introduced to carry out multi-angle comparative research.The comparison of experimental results shows that for the semantic segmentation of multi-class concrete bridge defects images,when VGG16 is used as the backbone network of U-Net,it achieves the highest recognition accuracy with a Mean Intersection over Union(MIo U)of 80.37%and a Mean Pixel Accuracy(MPA)of 90.03%.The lightweight convolutional network MobileNetV2-DeeplabV3+significantly reduces the number of parameters,resulting in faster detection speed of 71.87 frames/s,making it suitable for real-time defects detection.After introducing the SE,CBAM,and CA attention modules,both VGG16-U-Net and MobileNetV2-DeeplabV3 have achieved higher recognition accuracy,of which,the CA module can better guide the model to identify the subtle concrete defects.
万方数据
维普
