Abstract

In the field of road damage detection, the accuracy of existing methods still requires further improvement, particularly for elongated cracks, which are crucial for ensuring driving safety and effective road maintenance. To address this limitation, a novel road damage detection algorithm is proposed based on direction awareness and feature equalization. Specifically, a Direction-aware Strip Convolution (DSC) module is constructed to effectively capture the geometric characteristics of elongated cracks and maintain computational efficiency, by integrating asymmetric strip convolution and depthwise separable convolution respectively. In addition, a Multi-level Feature Equalization (MFE) module is designed to address the complex morphology and significant scale variations of road damage during multi-level feature fusion. Specifically, a set of learnable spatial weighting parameters is introduced in this module, whose weighting coefficients are optimized across different network layers and adaptively generated, thereby modulating the contributions of multi-level features and promoting a more balanced multi-level feature representation. Experimental results on the RDD2022-based experimental dataset demonstrate that the proposed method improves mAP@50 by 5.8 percentage points and recall by 5.9 percentage points, while achieving a processing speed of 122 FPS. Notably, the proposed method improves the detection performance of elongated cracks and achieves relatively balanced performance gains across different road damage categories, compared with the baseline model.

IPC Classification

Keywords