边缘计算设备的轻量级路面裂检测模型
Zhuang Li1, Junjie Yang1, Heqi Wang2
1School of Computer Science, Northeast Electric Power University, Jilin, 132012, China.
Scientific reports
|November 1, 2025
概括
一个新的轻量级道路裂检测模型,YOLO-DGVG,使用可变形卷曲和优化的网络模块. 这种方法提高了准确性,并减少了模型大小,以有效地识别路面裂.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 道路基础设施维护 道路基础设施维护
背景情况:
- 道路表面裂纹检测面临诸如假阳性,错过检测和大型模型大小等挑战.
- 现有的模型在复杂的背景和各种裂纹形状下扎.
研究的目的:
- 提出一个轻量级和准确的道路裂检测模型,YOLO-DGVG.
- 为了提高检测性能,同时降低计算复杂性和参数数量.
主要方法:
- 引入可变形卷积 (DCNv2) 进入骨干网络 (C2f-DCNv2) 进行自适应形状调整.
- 在子网络中集成轻量级GSConv和VoVGSCSP模块,以增强特征提取并降低复杂性.
- 开发了一个分组卷积检测头 (GCH),以最大限度地减少参数体积.
主要成果:
- 在PID数据集上进行的废弃性研究显示,回忆 (0.3-1.6%) 和mAP的增加,与YOLOv8.8相比,参数减少了22.28%.
- 在UAPD,RDD2022和自定义数据集上的泛化实验证实了有效性.
- 在检测任务中,YOLO-DGVG的表现优于RT-DETR和YOLOv10.
- 在边缘设备上成功部署静态图像裂检测.
结论:
- YOLO-DGVG在道路裂检测准确性和效率上提供了显著的改进.
- 该模型的轻量级设计和增强的模块使其适合于现实世界的应用,包括边缘计算.
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