A Lightweight Selective Feature Fusion and Irregular-Aware Network for Crack Detection Based on Federated Learning

With the extensive application of artificial intelligence, the detection accuracy can be significantly improved by using deep learning technology to detect cracks in road conditions and using federated learning to realize data security sharing. However, the existing methods usually have high model complexity, which make it difficult to deploy them on resource-constrained devices, such as mobile and automobile devices. In order to solve this problem, this paper proposes a light weight crack detection method. The improved method extracts multi-scale features through the lightweight encoder module, and further achieves effective fusion and efficient modeling of features through selective feature fusion blocks and irregular-aware blocks. In addition, it is the first time to combine federated learning with crack detection to collect and share data on the premise of ensuring data security. The experiment proves that our model achieves similar effect on the F1-score. The number of parameters and model size are reduced by 67.6% and 67%, respectively.