Accurate and efficient detection of road damage is essential for maintaining road safety and supporting intelligent transportation systems. While recent approaches leverage deep learning-based object detection frameworks, they often struggle with high computational demands and suboptimal feature extraction in complex environments. To address these challenges, we propose an enhanced object detection network for road damage detection based on the YOLOv8 architecture. Specifically, we integrate the Spatial and Channel Reconstruction Convolution (SCConv) module into the backbone to reduce feature redundancy while improving spatial and channel representation through a separation-reconstruction strategy. To enhance multi-scale feature fusion, we incorporate the Efficient Multi-Scale Attention (EMA) module into the neck, enabling adaptive spatial-channel attention without introducing significant computational overhead. Extensive benchmark comparison identifies YOLOv8l as a strong baseline for road damage detection. Building upon this, we embed SCConv within the original convolutional modules to achieve a lightweight network and enhance feature representation. We further explore multiple EMA integration strategies to identify an effective model configuration. Experimental results demonstrate that our best-performing model achieves higher detection accuracy than the baseline model, while maintaining computational efficiency.
Tang et al. (Mon,) studied this question.