深度学习论文： Computer Vision for Road Imaging and Pothole Detection: A State-of-the-Art Review of Systems and Algorithms Computer Vision for Road Imaging and Pothole Detection: A State-of-the-Art Review of Systems and Algorithms PDF: https://arxiv.org/pdf/2204.13590.pdf PyTorch代码: https://github.com/shanglianlm0525/CvPytorch PyTorch代码: https://github.com/shanglianlm0525/PyTorch-Networks
1 概述
本文详细介绍了道路成像传感器、坑检测算法和开源数据集。 
2 Road Imaging Systems
常用的道路成像系统包括：Laser scanners， Microsoft Kinect sensors 和 Stereo Camera(s)。 此外，还有基于多视角几何原理的单目运动相机或多目相机。
3 Road Pothole Detection Approaches
3-1 Classical 2-D Image Processing
传统的2D图像处理主要涉及四个阶段:(1) image pre-processing, (2) image segmentation, (3) damaged area extraction, and (4) detection result post-processing。
3-2 3-D Point Cloud Modeling and Segmentation
3-D road point clouds方法主要分为两个阶段:(1) interpolating the observed 3-D road point cloud into an explicit geometric model (typically a planar or quadratic surface), and (2) segmenting the observed 3-D road point cloud by comparing it with the interpolated geometric model.
3-3 Machine/Deep Learning
3-3-1 Image Classification-Based Methods
3-3-2 Object Detection-Based Methods
3-3-3 Semantic Segmentation-Based Methods
3-4 Hybrid Methods
Hybrid 地坑缺陷通常采用两种或两种以上算法发现。
4 Public Datasets
1 road image classification 2 instance-level pothole detection 3 Indian roads with semantic segmentation 4 CIMAT Challenging Sequences for Autonomous Driving (CCSAD) 5 Japan road damage dataset 6 automatic pothole detection and localization in urban streets 7 binary road image classification 8 multi-modal road pothole detection dataset 9 Pothole-600