Rethinking Road Surface 3D Reconstruction and Pothole Detection: From Perspective Transformation to Disparity Map Segmentation

• URL: http://arxiv.org/abs/2012.10802v2
• Date: Thu, 31 Dec 2020 02:54:59 GMT
• Title: Rethinking Road Surface 3D Reconstruction and Pothole Detection: From Perspective Transformation to Disparity Map Segmentation
• Authors: Rui Fan, Umar Ozgunalp, Yuan Wang, Ming Liu, Ioannis Pitas
• Abstract summary: Pothole detection is typically performed by structural engineers or certified inspectors. This paper presents an efficient pothole detection algorithm based on road disparity map estimation and segmentation.
• Score: 34.27692655476825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent [57.64826450787237]
  We show how to analyze the behavior of distributed gradient descent algorithms in the presence of adversarial corruptions. We show how to use ideas from (lazy) mirror descent to design a corruption-tolerant distributed optimization algorithm. Experiments based on linear regression, support vector classification, and softmax classification on the MNIST dataset corroborate our theoretical findings.
  arXiv  Detail & Related papers  (2024-07-19T08:29:12Z)
• Decoupling the Curve Modeling and Pavement Regression for Lane Detection [67.22629246312283]
  curve-based lane representation is a popular approach in many lane detection methods. We propose a new approach to the lane detection task by decomposing it into two parts: curve modeling and ground height regression.
  arXiv  Detail & Related papers  (2023-09-19T11:24:14Z)
• RoadScan: A Novel and Robust Transfer Learning Framework for Autonomous Pothole Detection in Roads [0.0]
  This research paper presents a novel approach to pothole detection using Deep Learning and Image Processing techniques. The system aims to address the critical issue of potholes on roads, which pose significant risks to road users.
  arXiv  Detail & Related papers  (2023-08-07T10:47:08Z)
• RCLane: Relay Chain Prediction for Lane Detection [76.62424079494285]
  We present a new method for lane detection based on relay chain prediction. Our strategy allows us to establish new state-of-the-art on four major benchmarks including TuSimple, CULane, CurveLanes and LLAMAS.
  arXiv  Detail & Related papers  (2022-07-19T16:48:39Z)
• Graph Attention Layer Evolves Semantic Segmentation for Road Pothole Detection: A Benchmark and Algorithms [34.80667966432126]
  Existing road pothole detection approaches can be classified as computer vision-based or machine learning-based. The latter approaches generally address road pothole detection using convolutional neural networks (CNNs) in an end-to-end manner. We propose a novel CNN layer, referred to as graph attention layer (GAL), which can be easily deployed in any existing CNN to optimize image feature representations for semantic segmentation.
  arXiv  Detail & Related papers  (2021-09-06T19:44:50Z)
• CP-loss: Connectivity-preserving Loss for Road Curb Detection in Autonomous Driving with Aerial Images [10.300623192980753]
  Road curb detection is important for autonomous driving. Most of the current methods detect road curbs online using vehicle-mounted sensors, such as cameras or 3-D Lidars. In this paper, we detect road curbs offline using high-resolution aerial images.
  arXiv  Detail & Related papers  (2021-07-26T01:36:58Z)
• Real-Time Pothole Detection Using Deep Learning [0.0]
  This study deployed and tested on different deep learning architecture to detect potholes. The images used for training were collected by cellphone mounted on the windshield of the car. The system was able to detect potholes from a range on 100 meters away from the camera.
  arXiv  Detail & Related papers  (2021-07-13T19:36:34Z)
• DAGMapper: Learning to Map by Discovering Lane Topology [84.12949740822117]
  We focus on drawing the lane boundaries of complex highways with many lanes that contain topology changes due to forks and merges. We formulate the problem as inference in a directed acyclic graphical model (DAG), where the nodes of the graph encode geometric and topological properties of the local regions of the lane boundaries. We show the effectiveness of our approach on two major North American Highways in two different states and show high precision and recall as well as 89% correct topology.
  arXiv  Detail & Related papers  (2020-12-22T21:58:57Z)
• Map-Enhanced Ego-Lane Detection in the Missing Feature Scenarios [26.016292792373815]
  This paper exploits prior knowledge contained in digital maps, which has a strong capability to enhance the performance of detection algorithms. In this way, only a few lane features are needed to eliminate the position error between the road shape and the real lane. Experiments show that the proposed method can be applied to various scenarios and can run in real-time at a frequency of 20 Hz.
  arXiv  Detail & Related papers  (2020-04-02T16:06:48Z)
• Road Curb Detection and Localization with Monocular Forward-view Vehicle Camera [74.45649274085447]
  We propose a robust method for estimating road curb 3D parameters using a calibrated monocular camera equipped with a fisheye lens. Our approach is able to estimate the vehicle to curb distance in real time with mean accuracy of more than 90%.
  arXiv  Detail & Related papers  (2020-02-28T00:24:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.