Channel Segmentation Proofreading Network for Crack Counting with Imbalanced Samples

This paper presents a channel segmentation proofreading network for crack counting with imbalanced samples. The network is built by stacking basic blocks called channel segmentation proofreading blocks, which are composed of the Approximate Overlapping Window Transformer and the Counting Proofreading Module. The former is designed to extract sufficient high-level semantic information, enhancing the ability of the network to judge crack quantities. Guided by the calculation results of the self-attention mechanism in the classical Transformer, Approximate Overlapping Window Transformer employs distinct computation steps to obtain the same results. Confining the computation process within overlapping windows, we continuously adjust to obtain the most suitable feature extraction process and internal structure for crack counting. Furthermore, to prevent the misidentification of multiple cracks as a single crack due to incorrect connection predictions of crack regions, the Counting Proofreading Module employs channel separation techniques. Following the concept of splitting positive and negative weights, it constructs positive and negative values with different characteristics, further confirming crack regions. Through the combined action of both components, when trained and tested on the crack counting dataset, our network achieves optimal results across all metrics.