A Road Crack Detection Method Based on Residual and Attention Mechanism

This paper proposes a road crack detection method based on residual and attention mechanisms to address the issues of difficult detection of small cracks on road surfaces in complex backgrounds and inaccurate crack detection edges. This method introduces residual modules in the encoder stage to better extract crack detail features and introduces attention mechanism modules in the skip connection structure of the network to better locate crack positions. Training and testing on public datasets have shown that compared with existing partial detection methods, our method has improved segmentation accuracy and generalization, and is more precise in segmenting small cracks, thus verifying the superiority of the proposed method in this paper.