Estimation of pavement crack ratio by top-view transformation of in-vehicle smartphone camera and deep learning-based classification
Estimation of pavement crack ratio by top-view transformation of in-vehicle smartphone camera and deep learning-based classification
复制标题
通过车载智能手机摄像头的俯视变换和基于深度学习的分类来估计路面裂缝率
DOI：
10.11532/jsceiii.3.3_26
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Michihiro NAKA
中科院分区：
文献类型：
--
作者：
Jose Maria Guyamin GEDA;Kai XUE;Tomonori NAGAYAMA;Boyu ZHAO;Michihiro NAKA
在日本，传统的道路裂纹率评估使用从精密线扫描相机获得的路面俯视图像，并基于裂缝数量手动对0.5m网格的路面进行分类。虽然已经提出了使用车载智能手机摄像头进行廉价的裂缝评估，但以前的工作无法根据指数定义计算裂缝比率，并且精度有限。针对车载智能手机摄像头拍摄的视频，提出了一种基于视觉的顶视变换和图像拼接算法，用于评价道路裂纹率。使用四种条件进行参数校准：1)水平检修孔轴线，2)平行车道线，3)圆形检修孔，4)垂直车道线条件。在顶视变换成功后，对连续的帧对进行特征匹配，计算两幅图像之间的单应矩阵，用于连续帧的图像拼接，得到图像之间的平移偏移。基于计算的平移偏移和提取的帧距离间隔，计算像素到实际距离的转换系数。根据索引定义，图像被划分为0.5米的网格。训练图像分类模型，根据裂缝的个数对每个网格盒进行分类。结果表明：1)车载智能手机摄像头采集的连续图像可以生成高分辨率的道路俯视图像；2)车载智能手机摄像头可以自动准确地估计路面的裂纹率。
The conventional road crack ratio evaluation in Japan uses top-view images of pavement obtained from precision line scan cameras and manually classifies 0.5 m grids of pavement surface based on the number of cracks. While inexpensive crack evaluation using in-vehicle smartphone cameras has been proposed, previous work cannot calculate the crack ratio based on the index definition and have limited accuracy. This paper proposes a vision-based top-view transformation and image stitching algorithm for road crack ratio evaluation using video captured by an in-vehicle smartphone camera. Four conditions are used to perform the parameter calibration: 1) horizontal manhole axis, 2) parallel lane line, 3) circular manhole, 4) vertical lane line conditions. After the successful top-view transformation, feature matching is conducted to pairs of successive frames to calculate the homography matrix between the two images, which is used for the image stitching of successive frames and obtaining the translation offset between the images. Based on the calculated translation offset and the extracted frame distance interval, the pixel-to-real-distance conversion factor is calculated. The image is divided into a 0.5 m grid based on the index definition. An image classification model was trained to classify each grid box according to the number of cracks. The results showed that: 1) a fine-resolution image of road top-view can be produced from successive images captured by an in-vehicle smartphone camera, and 2) the crack ratio can be accurately estimated from these images automatically.