用深度学习交叉验证提升道路地下缺陷识别准确率
Automatic Road Subsurface Distress Recognition from Ground Penetrating Radar Images using Deep Learning-based Cross-verification
- 设计跨视角交叉验证机制，融合多视角检测结果
- 召回率达98.6%，现场测试可减少90%人工工作量
- 适合交通巡检、智能养护等工程应用
地面穿透雷达（GPR）已成为道路地下病害（RSD）检测的快速无损方法。然而，从GPR图像中识别病害耗时且依赖专家经验。基于深度学习的自动识别虽减轻了数据处理负担，但缺陷识别能力仍不足。本研究提出一种新型交叉验证策略，充分利用区域建议网络在不同视角下对目标识别的互补能力。基于该策略，采用三个基于YOLO的模型检测空洞、松散结构和井盖，每个模型分别训练于包含2134个经实地验证样本的3D GPR数据集的不同视角。在真实现场扫描数据测试中，该方法召回率超过98.6%。现场测试表明，深度学习自动识别可使人工巡检工作量减少约90%。
原文摘要 · Abstract (English)
Ground penetrating radar (GPR) has become a rapid and non-destructive solution for road subsurface distress (RSD) detection. However, recognizing RSD from GPR images is labor-intensive and heavily relies on the expertise of inspectors. Deep learning-based automatic RSD recognition, though ameliorating the burden of data processing, suffers from insufficient capability to recognize defects. In this study, a novel cross-verification strategy was proposed to fully exploit the complementary abilities of region proposal networks in object recognition from different views of GPR images. Following this strategy, three YOLO-based models were used to detect the RSD (voids and loose structures) and manholes. Each model was trained with a specific view of 3D GPR dataset, which contains rigorously validated 2134 samples of diverse types obtained through field scanning. The cross-verification strategy achieves outstanding accuracy with a recall of over 98.6% in the tests using real field-scanning data. Field tests also show that deep learning-based automatic RSD recognition can reduce the human labor of inspection by around 90%.
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