改进的YOLOv8图像模型在城市人井盖缺陷管理和检测中的应用:案例研究
Yanqiong Ding1, Baojiang Han1,2, Hua Jiang2
1Beijing Information Infrastructure Construction Co., Ltd., Beijing 100080, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
改进的YOLOv8模型增强了城市人井盖缺陷检测,以98.6%的准确度识别破裂,损失和移位. 这种智能系统提高了关键基础设施管理的效率.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 城市基础设施管理 城市基础设施管理
背景情况:
- 对于不断扩大的城市环境而言,传统的手工检查漏洞盖是低效和不准确的.
- 现有的方法很难适应孔盖维护的复杂性.
研究的目的:
- 开发一种智能管理模型,用于检测常见的孔盖缺陷 (断裂,损失,移位).
- 利用人工智能提高城市基础设施检查的准确性和效率.
主要方法:
- 一个改进的YOLOv8模型,采用轻量级的双流网络与EfficientNetV2骨干.
- 整合了MBConv结构,前景注意模块和NAS-FPN,以增强特征提取和融合.
- 一个组合损失函数 (GIoU, BCE, DFL) 来处理样本失衡和类间差异.
主要成果:
- 拟议的模型实现了98.6%的总体识别准确度,用于孔盖缺陷.
- 在分类准确性,回忆率和F1分数方面表现出色.
- 在识别各种尺度的缺陷和增强多尺度特征表达性方面显著改进.
结论:
- 改进的YOLOv8模型为城市人孔盖缺陷识别提供了高度准确和高效的解决方案.
- 这种智能系统可以大大提高城市人坑盖的智能管理系统的效率.
- 由人工智能驱动的方法解决了传统手动检查方法的局限性.
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