基于SEA-YOLO v8的道路表面损坏检测研究
Yuxi Zhao1, Baoyong Shi2, Xiaoguang Duan3
1Jinan Zhuolun Intelligent Transportation Technology Co., LTD, Jinan, Shandong, China.
PloS one
|June 18, 2025
概括
本研究介绍了SEA-YOLO v8模型,用于有效检测道路损坏. 它提高了准确性和实时性能,帮助交通安全和道路维护工作.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 道路基础设施管理 道路基础设施管理
背景情况:
- 现有的道路损坏检测方法缺乏准确性,实时能力和适应性.
- 交通安全和道路维护是受这些限制影响的关键领域.
研究的目的:
- 开发一种先进的道路损坏检测模型 (SEA-YOLO v8),解决目前的技术缺陷.
- 为了提高检测准确度,实时处理和模型适应性,用于复杂的城市环境.
主要方法:
- 构建了SBS模块以优化计算复杂性并减少模型参数以实现轻量化设计.
- 将EMA注意力机制集成到部部件中,以改善特征表示和选择性聚焦.
- 提出了适应性注意力特征金字塔结构,以增强特征融合能力.
- 引入了轻量级的共享卷积检测头 (LSCD-Head),以进一步完善特征表示和减少参数.
主要成果:
- 在RDD2022数据集中,SEA-YOLO v8模型实现了63.2%的mAP50.
- 与标准YOLOv8模型和其他主流目标检测模型相比,表现出卓越的性能.
- 在复杂的城市交通场景中验证了高检测准确性和适应性.
结论:
- 该SEA-YOLO v8型号提供准确的道路损坏定位和检测.
- 该模型有助于在道路评估和维护中节省人力和物质资源.
- 通过改善基础设施管理,促进可持续的城市道路发展.
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