路面-DETR:用于路面缺陷检测的高精度实时检测变压器
Cuihua Zuo1, Nengxin Huang1, Cao Yuan1
1School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430024, China.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
这项研究介绍了Pavement-DETR,一种改进的道路缺陷检测模型. 它提高了各种路面问题的准确性,大大提高了道路维护的安全性和效率.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 土木工程 土木工程是指土木工程.
背景情况:
- 准确的道路缺陷检测对于道路维护的安全性和效率至关重要.
- 挑战包括区分前景/后景和识别小或重叠的缺陷.
研究的目的:
- 为准确的道路缺陷检测提出一个优化的路面-DETR模型.
- 为了提高检测常见的路面缺陷,如裂和坑洞.
主要方法:
- 在ResNet骨干中引入了道空间混合 (CSS) 注意力,以精确的特征聚焦.
- 采用Conv3XC结构,以改善前景和背景的差异化和特征融合.
- 开发了一个使用PIoU v2和规范化瓦瑟斯坦距离 (NWD) 进行综合界限框优化的综合损失函数.
主要成果:
- 在UAV-PDD2023数据集上,Pavement-DETR实现了7.7%的mAP改进,IoU=0.5.
- 与基线相比,mAP在IoU=0.5-0.95时增加了8.9%,F1评分改善了7%.
- 在检测小尺寸和重叠的道路缺陷方面表现出卓越的性能.
结论:
- 路面-DETR模型显著提高了道路缺陷检测的准确性.
- 提议的改进有效地解决了识别各种复杂路面缺陷的挑战.
- 这种模式为高效和安全的道路维护运营提供了巨大的好处.
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