Research on lightweight GPR road surface disease image recognition and data expansion algorithm based on YOLO and GAN AITranslate

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DOI：https://doi.org/10.1016/j.cscm.2023.e02779

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The aim of this paper is to improve the accuracy and efficiency of ground penetrating Radar (GPR) detection of internal road surface disease images. Based on the YOLOv4 target detection algorithm, this study introduces MobileNetV2 and CBAM attention mechanism, and combines the Focal loss confidence loss function to iterate the model, so as to design an efficient and lightweight GPR pavement disease image recognition algorithm, MC-YOLOv4. At the same time, in order to alleviate the problem of data scarcity in GPR, we redesign an unsupervised generative adversarial neural network based on self-attention mechanism, namely SAGAN-W. Experiments show that MC-YOLOv4 not only has faster reasoning ability, but also occupies only 23% of the memory of YOLOv5-S. After using the SAGAN data augmentation, the mAP@ .0.5 evaluation index is further improved by 2.63%, and the collapse and mode collapse that may occur in the training process of the traditional Convolutional Generative Adversarial Neural Network (DCGAN) are avoided. After introducing the Focal loss confidence loss function to participate in the training, It significantly improves the imbalance between the precision and recall of the detection model, and this scheme is verified and supported by real scenes. The experimental results show that the proposed method has significant advantages in automatic detection and data expansion of lightweight GPR pavement invisible diseases, which has a wide range of application value and research significance.