Abstract: Aiming at the problems of complex models and slow processing speed of pavement crack detection model based on deep learning in embedded platform deployment and application,a pavement crack detection method for embedded application is proposed based on YOLO.Firstly,the two-stage concatenated convolutional module is constructed in the backbone network to optimize the feature perception of the convolution channel and the input space,and the Smooth Intersection over Union(SIoU) considering the vector angle between the prediction bounding boxes and ground truth bounding boxes is used as the loss function of the network to improve the accuracy and speed of the prediction bounding boxes regression.Secondly,a method of the cascade channel pruning and convolution-by-convolution weight pruning is proposed,and channel pruning and weight pruning are performed successively,the unnecessary channels are removed and the redundant weights are removed by convolution,which significantly compresses the model while ensuring the stability of the model.Then,distill the generalization ability of the YOLOv5l model for crack features into the pruned model to further improve its ability to characterize cracks.Finally,under the TensorRT engine,the inference speed of the model is improved through layer optimization and other methods.The experimental results show that compared with the original model,the mean Average Precsion of the proposed method is increased by 2.7%,the model parameters and model volume are reduced by 78.24% and 76.13% respectively,and the detection rate is increased by 587.88 %.The model is deployed on NVIDIA Jetson Nano embedded devices through TensorRT for testing,the detection accuracy is unaffected,and the detection rate is increased by 140%,compared with the commonly used models such as YOLOv5-Lite and the la-test models such as YOLOv10 and RT-DERT,it has the highest detection accuracy and the most significant lightweight effect,which is suitable for embedded applications.

Key words: Pavement crack detection, Embedded applications, Two-stage concatenated convolutional module, Loss function, Cascade channel pruning and convolution-by-convolution weight pruning, Knowledge distillation