Automatic Pavement Crack Detection Transformer Based on Convolutional and Sequential Feature Fusion

Abstract

To solve the problem of low accuracy of pavement crack detection caused by natural environment interference, this paper designed a lightweight detection framework named PCDETR (Pavement Crack DEtection TRansformer) network, based on the fusion of the convolution features with the sequence features and proposed an efficient pavement crack detection method. Firstly, the scalable Swin-Transformer network and the residual network are used as two parallel channels of the backbone network to extract the long-sequence global features and the underlying visual local features of the pavement cracks, respectively, which are concatenated and fused to enrich the extracted feature information. Then, the encoder and decoder of the transformer detection framework are optimized; the location and category information of the pavement cracks can be obtained directly using the set prediction, which provided a low-code method to reduce the implementation complexity. The research result shows that the highest AP (Average Precision) of this method reaches 45.8% on the COCO dataset, which is significantly higher than that of DETR and its variants model Conditional DETR where the AP values are 36.9% and 42.8%, respectively. On the self-collected pavement crack dataset, the AP of the proposed method reaches 45.6%, which is 3.8% higher than that of Mask R-CNN (Region-based Convolution Neural Network) and 8.8% higher than that of Faster R-CNN. Therefore, this method is an efficient pavement crack detection algorithm.