Comparative experiment.

Road damage detection is of great significance to traffic safety and road maintenance. However, the existing target detection technology still has shortcomings in accuracy, real-time and adaptability. In order to meet this challenge, this study constructed SEA-YOLO v8 model for road damage detection. Firstly, the SBS module is constructed to optimize the computational complexity, achieve real-time target detection under limited hardware resources, successfully reduce the model parameters, and make the model more lightweight; Secondly, we integrate the EMA attention mechanism module into the neck component, enabling the model to utilize feature information from different layers, enabling the model to selectively focus on key areas and improve feature representation; Then, an adaptive attention feature pyramid structure is proposed to enhance the feature fusion capability of the network; Finally, lightweight shared convolutional detection head (LSCD-Head) is introduced to improve feature representation and reduce the number of parameters. The experimental results on the RDD2022 dataset show that the SEA-YOLO v8 model has achieved 63.2% mAP50. The performance is better than yolov8 model and mainstream target detection model. This shows that in complex urban traffic scenarios, the model has high detection accuracy and adaptability, can accurately locate and detect road damage, save manpower and material resources, provide guidance for road damage assessment and maintenance, and promote the sustainable development of urban roads.

道路损伤检测对于交通安全与道路养护具有重要意义。然而，现有目标检测技术在精度、实时性与适应性方面仍存在不足。为应对这一挑战，本研究构建了SEA-YOLO v8模型用于道路损伤检测。首先，构建SBS模块以优化计算复杂度，实现在有限硬件资源下的实时目标检测，成功降低模型参数量，使模型更轻量化；其次，将EMA注意力机制模块集成至颈部组件中，使模型能够利用不同层级的特征信息，选择性聚焦关键区域并提升特征表征能力；随后，提出自适应注意力特征金字塔结构以增强网络的特征融合能力；最后，引入轻量化共享卷积检测头（LSCD-Head）以提升特征表征能力并降低参数量。在RDD2022数据集上的实验结果表明，SEA-YOLO v8模型的mAP50达到63.2%，其性能优于YOLOv8模型与主流目标检测模型。这表明，在复杂城市交通场景中，该模型具备较高的检测精度与适应性，能够精准定位并检测道路损伤，节省人力与物力资源，为道路损伤评估与养护提供指导，助力城市道路的可持续发展。