论文标题
重新思考道路表面3D重建和坑洼检测：从透视转换到差异图分段
Rethinking Road Surface 3D Reconstruction and Pothole Detection: From Perspective Transformation to Disparity Map Segmentation
论文作者
论文摘要
坑洼是道路损坏的最常见形式之一，可能会严重影响驾驶舒适性，道路安全性和车辆状况。坑洼检测通常由结构工程师或认证检查员进行。但是，这项任务不仅对人员有害，而且非常耗时。本文基于道路差异图的估计和分段提出了一种有效的坑洼检测算法。我们首先通过结合立体声钻机卷角来概括透视转换。然后使用半全球匹配估算道路差异。然后执行差异图转换算法，以更好地区分损坏的道路区域。最后，我们利用简单的线性迭代聚类将转换的差异分组为超像素的集合。然后通过找到超级像素来检测坑洼，其值低于自适应确定的阈值。所提出的算法是在CUDA的NVIDIA RTX 2080 TI GPU上实现的。该实验证明了我们提出的道路坑孔检测算法的准确性和效率，该算法的准确性为99.6％，F评分达到89.4％。
Potholes are one of the most common forms of road damage, which can severely affect driving comfort, road safety and vehicle condition. Pothole detection is typically performed by either structural engineers or certified inspectors. This task is, however, not only hazardous for the personnel but also extremely time-consuming. This paper presents an efficient pothole detection algorithm based on road disparity map estimation and segmentation. We first generalize the perspective transformation by incorporating the stereo rig roll angle. The road disparities are then estimated using semi-global matching. A disparity map transformation algorithm is then performed to better distinguish the damaged road areas. Finally, we utilize simple linear iterative clustering to group the transformed disparities into a collection of superpixels. The potholes are then detected by finding the superpixels, whose values are lower than an adaptively determined threshold. The proposed algorithm is implemented on an NVIDIA RTX 2080 Ti GPU in CUDA. The experiments demonstrate the accuracy and efficiency of our proposed road pothole detection algorithm, where an accuracy of 99.6% and an F-score of 89.4% are achieved.
