Disparity Map-Crack Detection: Combining Disparity Map Feature into Binary Segmentation for Accurate Crack Detection

To address the limitations of crack detection methods relying solely on RGB images, we propose an in-novative approach that incorporates disparity maps as an additional data source. This integration with RGB images aims to enhance crack detection performance. However, the generation of disparity maps is susceptible to image noise and matching errors, leading to inaccurate or mismatched disparity values that may impede precise ground crack detection. To mitigate this challenge, we apply a disparity transformation technique to refine the estimated disparity map, improving the differentiation of crack regions. Additionally, we employ a feature fusion method based on a connected low-loss subspace. This approach adaptively assigns feature weights to facilitate the complementary fusion of disparity and RGB features. Fur-thermore, the decoder includes a multi-scale feature alignment module that uses the fused encoder features to align each layer in the decoding process. This preserves image details and local features, enhancing the overall detection accuracy. Extensive testing experiments demonstrate a significant breakthrough in crack detection performance, achieving an Intersection over Union (IoU) of 80.48%. Our approach sets the benchmark in crack detection, effectively leveraging multi-source information, mitigating disparity map noise, and enhancing feature fusion.