Data-Driven Pseudo-Crack Cognition and Removal for Intelligent Pavement Inspection with Gradient Priority and Self-Attention

Road surface cracks are the most common and significant diseases in pavement inspection. However, the presence of crack-like edges on objects such as water stains, fallen leaves, and ruts often result in the false detection of road cracks. To better recognize pseudo-cracks, we first construct a novel dataset containing real pseudo-crack images for training and evaluation. To distinguish pseudo-cracks within images, a gradient prior is introduced to enhance the network's perception of the detailed changes in crack edges, thereby improving its crack localization capability. Next, a self-attention mechanism is employed to focus on the extraction of global crack features, effectively mitigating interference from pseudo-crack features. Subsequently, deep global semantic features are fused with shallow detail features through dense connections, enriching feature extraction while circumventing the issue of edge gradient disappearance often encountered in deeper networks. Finally, the concatenation of deep global features with shallow detail features enhances the utilization of effective features, enabling robust pseudo-crack removal and preserving the continuity and integrity of the detected cracks. To validate the effectiveness of the proposed approach, we conduct comparative experiments with several crack detection methods across multiple dataset. The results demonstrate that our method achieves superior performance in both quantitative indicators and visual effects.