RENet is proposed for accurate and robust pavement crack detection. The rectangular convolution pyramid module is first built on deep layers so that the features can describe defects with different structures. The optimized contextual information and features of shallower layers are gradually merged into three resolutions. Subsequently, the hierarchical feature fusion refinement module and the boundary refinement module are applied to each branch. These two modules effectively promote the seamless fusion of features at various scales and make the model pay more attention to boundaries. Finally, the outputs of the three branches are integrated to obtain the final prediction map.

This paper proposes a relevance-aware and cross-reasoning network (RCN) for anomaly segmentation of pavement defects, which can segment defects using merely non-defective images for training. A relevance-aware transformer-based encoder is first devised to model intrinsic interdependencies across local features, thus improving representations of complex non-defective images. Next, a dual decoder strategy is proposed to remap the encoder-generated latent dependencies at the local semantic and global detailed levels, respectively. Specifically, a cross-reasoning refinement module is built in the local decoder to reason the crossrelationship between spatial and channel dimensions. Finally, a context-aware abnormal distillation measurement is developed to evaluate the semantic reconstruction deviations during the inference. Under the guidance of semantic affinity, this measurement allows our model to highlight defective areas adaptively. Extensive experimental results on four datasets indicate that RCN outperforms other leading anomaly segmentation methods.