Road Surface Defect Detection -- From Image-based to Non-image-based: A Survey

Ensuring traffic safety is crucial, which necessitates the detection and prevention of road surface defects. As a result, there has been a growing interest in the literature on the subject, leading to the development of various road surface defect detection methods. The methods for detecting road defects can be categorised in various ways depending on the input data types or training methodologies. The predominant approach involves image-based methods, which analyse pixel intensities and surface textures to identify defects. Despite their popularity, image-based methods share the distinct limitation of vulnerability to weather and lighting changes. To address this issue, researchers have explored the use of additional sensors, such as laser scanners or LiDARs, providing explicit depth information to enable the detection of defects in terms of scale and volume. However, the exploration of data beyond images has not been sufficiently investigated. In this survey paper, we provide a comprehensive review of road surface defect detection studies, categorising them based on input data types and methodologies used. Additionally, we review recently proposed non-image-based methods and discuss several challenges and open problems associated with these techniques.Comment: Survey paper

Detection of Pothole by Applying Convolutional Neural Network and Random Forest Techniques

Roads are essential for daily transportation worldwide, but their aging and usage patterns can cause deterioration of the road surface, leading to a decline in quality. This deterioration often results in the formation of potholes and cracks on the roads, which can cause damage to vehicles or pose a physical danger to occupants, particularly in underdeveloped countries. Identifying potholes in real-time can help drivers avoid them and prevent accidents. Furthermore, recording their locations and sharing them can assist other drivers and road maintenance organizations take prompt corrective measures. In our attempt to address the issue of pothole detection, we aim to combine the latest technological advancements. We aim to develop practical, reliable, adaptable, and modular solutions. To achieve this, we will compare the performance of Random Forest, a machine learning model, with CNN, a deep learning model, in detecting potholes. We will train these models using multiple datasets and analyse their performance to determine their effectiveness in pothole detection

Towards Increasing the Robustness of Predictive Steering-Control Autonomous Navigation Systems Against Dash Cam Image Angle Perturbations Due to Pothole Encounters

Vehicle manufacturers are racing to create autonomous navigation and steering control algorithms for their vehicles. These software are made to handle various real-life scenarios such as obstacle avoidance and lane maneuvering. There is some ongoing research to incorporate pothole avoidance into these autonomous systems. However, there is very little research on the effect of hitting a pothole on the autonomous navigation software that uses cameras to make driving decisions. Perturbations in the camera angle when hitting a pothole can cause errors in the predicted steering angle. In this paper, we present a new model to compensate for such angle perturbations and reduce any errors in steering control prediction algorithms. We evaluate our model on perturbations of publicly available datasets and show our model can reduce the errors in the estimated steering angle from perturbed images to 2.3%, making autonomous steering control robust against the dash cam image angle perturbations induced when one wheel of a car goes over a pothole.Comment: 7 pages, 6 figure

Enhancing Road Infrastructure Monitoring: Integrating Drones for Weather-Aware Pothole Detection

The abstract outlines the research proposal focused on the utilization of Unmanned Aerial Vehicles (UAVs) for monitoring potholes in road infrastructure affected by various weather conditions. The study aims to investigate how different materials used to fill potholes, such as water, grass, sand, and snow-ice, are impacted by seasonal weather changes, ultimately affecting the performance of pavement structures. By integrating weather-aware monitoring techniques, the research seeks to enhance the rigidity and resilience of road surfaces, thereby contributing to more effective pavement management systems. The proposed methodology involves UAV image-based monitoring combined with advanced super-resolution algorithms to improve image refinement, particularly at high flight altitudes. Through case studies and experimental analysis, the study aims to assess the geometric precision of 3D models generated from aerial images, with a specific focus on road pavement distress monitoring. Overall, the research aims to address the challenges of traditional road failure detection methods by exploring cost-effective 3D detection techniques using UAV technology, thereby ensuring safer roadways for all users

Real-Time Stereo Vision for Road Surface 3-D Reconstruction

Stereo vision techniques have been widely used in civil engineering to acquire 3-D road data. The two important factors of stereo vision are accuracy and speed. However, it is very challenging to achieve both of them simultaneously and therefore the main aim of developing a stereo vision system is to improve the trade-off between these two factors. In this paper, we present a real-time stereo vision system used for road surface 3-D reconstruction. The proposed system is developed from our previously published 3-D reconstruction algorithm where the perspective view of the target image is first transformed into the reference view, which not only increases the disparity accuracy but also improves the processing speed. Then, the correlation cost between each pair of blocks is computed and stored in two 3-D cost volumes. To adaptively aggregate the matching costs from neighbourhood systems, bilateral filtering is performed on the cost volumes. This greatly reduces the ambiguities during stereo matching and further improves the precision of the estimated disparities. Finally, the subpixel resolution is achieved by conducting a parabola interpolation and the subpixel disparity map is used to reconstruct the 3-D road surface. The proposed algorithm is implemented on an NVIDIA GTX 1080 GPU for the real-time purpose. The experimental results illustrate that the reconstruction accuracy is around 3 mm.Comment: 6 pages, 4 figures, IEEE International Conference on Imaging System and Techniques (IST) 2018. arXiv admin note: substantial text overlap with arXiv:1807.0204

Texton Based Segmentation for Road Defect Detection from Aerial Imagery

Road defect such as potholes and road cracks, became a problem that arose every year in Indonesia. It could endanger drivers and damage the vehicles. It also obstructed the goods distribution via land transportation that had major impact to the economy. To handle this problem, the government released an online complaints system that utilized information system and GPS technology. To follow up the complaints especially road defect problem, a survey was conducted to assess the damage. Manual survey became less effective for large road area and might disturb the traffic. Therefore, we used road aerial imagery captured by Unmanned Aerial Vehicle (UAV). The proposed method used texton combined with K-Nearest Neighbor (K-NN) to segment the road area and Support Vector Machine (SVM) to detect the road defect. Morphological operation followed by blob analysis was performed to locate, measure, and determine the type of defect. The experiment showed that the proposed method able to segment the road area and detect road defect from aerial imagery with good Boundary F1 score