Enhanced Crack Segmentation Network: Leveraging Multi-Dimensional Attention

Ammar M. Okran, Hatem A. Rashwan, Domenec Puig

Abstract: Structural defects, such as cracks, are crucial in various infrastructures, with their accurate delineation paramount for maintenance. However, existing methods often struggle to precisely segment cracks. Despite the advent of deep learning in image segmentation, the recurrent convolution and pooling operations tend to overlook vital edge information, thus compromising the final segmentation accuracy. This paper proposes a pixel-level crack segmentation network using a UNet architecture with a pre-trained ConvNext as the encoder, combined with Multiple Dimension Attention Enhancement (MDAE) blocks. The MDAE block enhances local edge information acquisition, leading to more precise crack segmentation. Experimental results on a public dataset, Crack500, demonstrate the proposed network’s effectiveness, achieving an IoU of 59.6% and an F1-score of 74.7%, thus significantly improving crack segmentation performance.