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基于SEA-YOLO v8的道路表面损坏检测研究

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  • 1Jinan Zhuolun Intelligent Transportation Technology Co., LTD, Jinan, Shandong, China.

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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.

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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.