Low-Contrast Coating Surface Microcrack Detection Using an Improved U-Net Network Based on Probability Map Fusion.

To address challenges such as low contrast, complex backgrounds, and discontinuous crack distribution in coating surface microcrack detection, a detection method combining circular neighborhood features with an improved U-net is proposed. In the preprocessing stage, a background template is constructed via median filtering, and crack contrast is enhanced through a combination of difference operations and Gaussian smoothing. Based on the spatial aggregation and directionality of crack pixels, multi-scale and multi-directional circular scanning filters were constructed to generate neighborhood difference maps for quantifying the crack distribution probability. The ImF-Att-DO-U-net was designed by utilizing a dual-channel input consisting of the original image and the crack probability map. The encoder embeds lightweight CBAMs to strengthen crack features, while the decoder introduces DO-Conv and Leaky ReLU to enhance detail capture capabilities. A hybrid loss function combining Binary Cross-Entropy and Dice loss was employed to optimize class imbalance. Algorithm testing results demonstrate that the proposed method achieved a Dice coefficient of 0.884, an SSIM of 0.893, and an accuracy of 0.911, outperforming comparative models such as DO-U-net. The extraction rate for cracks ≥10 μm reached 98%, with a minimum detectable crack size at the 7 μm level. The method exhibited excellent robustness under noise and blur testing, demonstrating superior environmental adaptability.