A novel high-precision lightweight concrete bridge crack image recognition method based on knowledge distillation

Abstract

To meet the demand for high-precision recognition and real-time processing when using intelligent detection methods in daily concrete bridge crack detection tasks, a high-precision lightweight crack image recognition method based on knowledge distillation is proposed. Through semisupervised learning, the information embedded in unlabeled data is used to train a lightweight network to obtain high-precision crack recognition results. First, by comparing the recognition accuracy of lightweight network under different training strategies, the effectiveness of the knowledge distillation method in improving the accuracy of lightweight network is verified. Second, a label-free knowledge distillation method for engineering detection is proposed. Finally, using MobileNetv3 as the student model, under the self-built three-classification dataset of nearly 700,000 images, the accuracy reached 99.84% through the proposed knowledge distillation method, which is very close to the teacher model ResNeXt101 accuracy (−0.08%), and MobileNetv3 compared with ResNeXt101 its model inference speed increased by more than 13 times. At the same time, the MobileNetv3 model improves the inference speed by more than 13 times and reduces the number of model parameters by more than 35 times compared to the ResNeXt101 model. The research results fully demonstrate that under the premise of having a large amount of unlabeled data, using the proposed knowledge distillation method, the recognition accuracy of the trained lightweight model has a significant advantage over the existing lightweight network training strategies.