Road Crack Detection Based on Position Information and Attention Mechanism
Road cracks are the main cause of highway safety problems.Traditional crack detection is typically based on manual detection,which faces problems such as low efficiency and insecurity.In addition,the existing deep learning detection model causes incomplete crack detection when facing interference factors,such as shadow occlusion and complex backgrounds.To address these problems,a road crack detection model based on location information and an attention mechanism,known as PA-TransUNet,is proposed.First,the hybrid encoder receives the input image,extracts the crack feature information,and introduces the position information of the query,key,and value to improve the ability of the self-attention mechanism in the encoder Transformer to capture the crack shape and compensate for the loss of feature information.Subsequently,the crack features are input into the decoder for upsampling,and an Attention Gating-based Decoding Module(AGDM)is designed to strengthen the learning of crack regions by suppressing non-crack regions and improving the accuracy and integrity of crack detection.The experimental results demonstrate that the F1 values of the PA-TransUNet model on the CrackForest Dataset(CFD)and Cracktree200 public datasets reach 87.44％and 82.58％,respectively.In addition,to further test the crack detection ability of the PA-TransUNet model in practical engineering,an F1 value of 88.68％is achieved on the self-made Unmanned Aerial Vehicle Cracks(UAV Cracks)dataset,which shows that it can better meet the needs of crack detection in practical engineering.
万方数据
维普
