Pothole detection methods. .

Pothole detection methods. Apr 20, 2022 · Detailed real-time performance comparison of state-of-the-art deep learning models and object detection frameworks (YOLOv1, YOLOv2, YOLOv3, YOLOv4, Tiny-YOLOv4, YOLOv5, and SSD-mobilenetv2) for pothole detection is presented. Aug 19, 2025 · This study aims to detect potholes using Unmanned Aerial Vehicles (UAVs) images, enabling accurate analysis of their size, shape, and location, thereby enhancing detection efficiency compared to conventional methods. The detection process and technology proposed in the latest research related to automated pothole detection are described for each method. This literature review aims to give you the lowdown on all the different pothole detection methods out there – what works well, what doesn’t, and where we can improve. By analyzing various methodologies, including image processing techniques and machine learning algorithms, this review seeks to uncover the most effective strategies for detecting potholes with high precision and efficiency. Aug 30, 2024 · By reviewing and evaluating existing vision-based methods, this paper clarifies the current landscape of pothole detection technologies and identifies opportunities for future research and. It offers a comprehensive overview of cutting-edge pothole detection algorithms, categorizing them into four groups: traditional two-dimensional (2D) image processing, three-dimensional (3D) point cloud processing, machine learning/deep learning methods, and hybrid approaches. May 24, 2022 · In this paper, three methods are compared, and the strengths and weaknesses of each method are summarized. ekmqqecs jofs llm nydgty yncu xqovem cwvfw aixr wtibl cizl