基于探地雷达的路基脱空病害图像自动识别

道路病害快速检测对于确保道路的安全和可靠运行至关重要。而探地雷达技术在道路病害检测中具有快速、无损和高分辨率等特征，因此被广泛应用。然而，以往的雷达图像处理和解译主要依赖人员的主观经验，易导致误判和漏判。为了解决这一问题，通过研究基于YOLO算法的图像识别方法，结合深度学习技术，开发一种智能化的道路病害识别系统，能够自动提取探地雷达图像中各类病害的特征，并实现高效、智能的识别，并通过钻孔验证，以确保识别结果的准确性，有效预防突发性道路塌陷的发生，提高道路的安全性和可靠性。

Automated Recognition of Roadbed Cavity Disease Image Based on Ground Penetrating Radar

The rapid detection of road diseases is crucial to ensure the safety and reliable operation of roads. The ground penetrating radar (GPR) technology has been widely applied in road disease detection because of its speed, non-destructiveness, high resolution and other features. However, the previous radar image processing and interpretation were mainly relied on the subjective experience of personnel, which leads to the misjudgments and missed detections. To solve this problem, an intelligent road disease recognition system has been developed by studying the image recognition method based on YOLO algorithm and combined with the deep learning technology. This system can automatically extract the characteristics of various diseases from GPR images and achieve the efficient and intelligent recognition. And the accuracy of the recognition results is ensured through the borehole verification to effectively prevent the sudden road collapses and improve the road safety and reliability.