End-to-End Roadway Disease Recognition Based on Transformer Architecture

Road disease is a significant factor causing traffic accidents. Timely detection and recognition of road disease is significant for maintaining road safety and reducing traffic accidents. Therefore, it is urgent to study an automatic pavement disease recognition method with accuracy and real-time. However, existing real-time target detectors generally use a convolutional neural network-based architecture, and these detectors usually require post-processing of NMS, which makes the detectors difficult to optimize and unstable, resulting in a delay in reasoning speed. Therefore, we design a target detection model based on Transformer, which uses MobileNet as the backbone network to simplify the network structure and enables interaction and integration of features through an efficient hybrid encoder, which reduces the computational load and does not reduce the detection accuracy. Iou-aware query selection optimizes the object query vector in the Transformer structure and reduces the number of candidate boxes that the model needs to process. The experimental results show that we have achieved good results on the RDD2022 dataset.