A Lightweight Convolutional Transformer Architecture Approach for Crack Segmentation in Safety Assessment

Crack segmentation is a pivotal task in assessing structural integrity across diverse domains, ranging from civil infrastructure such as bridges and buildings to the fabrication of heavy vehicles, which is crucial for ensuring the longevity and safety of materials. Despite its critical importance, the domain remains relatively underexplored within the academic sphere, particularly in accommodating the crack segmentation on resource-constrained devices. This challenge arises due to the inherent demand for deeper and broader network structures to achieve optimal performance, resulting in heavier computational and storage overhead. Thus, deploying crack segmentation models on practical platforms poses a formidable challenge. This paper presents a novel lightweight hybrid framework comprising robust Attention UNet architecture to assimilate comprehensive contextual information alongside the efficient MobileVit block to extract and integrate global contextual information utilizing the intricate self-attention mechanism. The intensive experiment results illustrate that our proposed method outperforms the existing state-of-the-art methods on the public benchmark datasets despite employing a reduced parameter space. The dataset and code can be accessed at: https://github.com/REINS-SJTU/TransAUnet