Improved DETR Algorithm for Asphalt Pavement Damage Detection in UAV Aerial Images
Aiming at the problems of insufficient data,low Detection accuracy and missed detection of aerial images of asphalt pavement,an improved DETR(Detection Transformer)end-to-end asphalt pavement damage detection model is proposed.Firstly,the model uses ResNet50 to extract features,introduces the SiLU activation function to improve feature extraction ability,and uses a multi-scale fusion feature map to retain more context semantic information.Sec-ondly,the multi-scale deformable self-attention mechanism is used in the Transformer Encoder to accelerate the con-vergence speed of the model.Finally,the CIoU loss function is used to improve the accuracy of crack detection.The ex-perimental results show that the average precision of the improved model is 83.7%,which is 7.4%higher than that of the DETR model,and the recall rate is increased by 10.9%.The proposed improved model can effectively detect as-phalt pavement damage,which can provide a reference for the detection of asphalt pavement damage in aerial images.
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