Abstract
The segmentation accuracy of bridge crack images is influenced by high-frequency light, complex scenes, and tiny cracks. Therefore, an integration-competition network (complex crack segmentation network [CCSNet]) is proposed to address these problems. First, a grayscale-oriented adjustment algorithm is proposed to solve the high-frequency light problem. Second, an integration-competition mechanism is proposed to detach complex backgrounds and grayscale features of cracks. Finally, a tiny attention mechanism is proposed to extract the shallow features of tiny cracks. CCSNet outperforms seven state-of-the-art crack segmentation methods in both generalization and comparison experiments on self-built dataset and four public datasets. It also achieved excellent performance in practical bridge crack tests. Therefore, CCSNet is an effective auxiliary method for lowering the cost of bridge safety detection.