A crack detection model fusing local details and global context for nuclear cladding coating surfaces

Crack detection on the surface of nuclear cladding coatings is critical for ensuring the safe operation of nuclear power plants. However, due to the imbalance between crack and background pixels, complex crack morphology, numerous interfering factors, and the subtle features of fine cracks in nuclear cladding coating surface images, the detection performance of existing methods remains unsatisfactory. To address these issues, this paper proposes a novel crack detection model for nuclear cladding coatings surfaces, named CrackCTFuse. This model effectively captures both local detailed features and global context in crack images. Additionally, a crack local feature enhancement module is designed to supplement and enhance the edge details information of cracks, and a crack feature fusion module is proposed to facilitate the effective integration of local and global features. Moreover, a multi-scale convolutional attention module based on channel segmentation is developed to aggregate multi-scale contextual information, enhance skip connections, and improve the model's ability to perceive and represent crack features at various scales. Experiments conducted on the constructed nuclear cladding coating surfaces crack dataset demonstrate the effectiveness and accuracy of the CrackCTFuse model, achieving a MIoU of 92.70% and an F1-score of 92.54%.