We propose a fully autonomous robust real-time road crack and pothole detection algorithm which can be deployed on any GPU based conventional processing boards with an associated camera. The approach is based on a deep neural net architecture which detects cracks and potholes using texture and spatial features.

Jan 24, 2023 · Understanding the need for an automated system for the detection of cracks and potholes, this study proposes a decision support system (DSS) for an autonomous road information system for smart city development with the use of deep learning.

Oct 6, 2023 · The present article proposes the deep learning concept termed ―Faster-Region Convolutional Neural Network‖ (Faster-RCNN) technique to detect cracks on road for autonomous cars. Feature...

With the advancements in computer vision and deep learning, automated detection systems offer a promising solution. This paper explores the application of various YOLO (You Only Look Once) models for real-time pothole detection.

3 days ago · Road surface pothole detection is crucial for ensuring the driving safety and path planning of autonomous vehicles. However, existing detection methods are often affected by variations in lighting, weather conditions, and complex environments, resulting in lower detection precision and recall rates. To address this, this paper proposes an innovative improved algorithm, which is based on the ...

Deep learning techniques have emerged as promising solutions for automating pothole detection processes. By leveraging convolutional neural networks (CNNs) and other advanced algorithms, these techniques enable the extraction of intricate features from road images, facilitating accurate identification of potholes.

Sep 9, 2018 · We propose a fully autonomous robust real-time road crack and pothole detection algorithm which can be deployed on any GPU based conventional processing boards with an associated camera. The approach is based on a deep neural net architecture which detects cracks and potholes using texture and spatial features.

The authors proposed a deep learning approach for automatic road surface monitoring and pothole detection. The paper applies different deep learning models, including convolutional neural networks (CNN), LSTM networks, and reservoir computing models.

Dec 1, 2018 · Sukhad Anand et al. [7] proposed a new approach for autonomous pothole and crack recognition that works in real time, and can tolerate major background noise, viewpoint changes,...

Feb 10, 2025 · In this study, we offer a unique, efficient and accurate road crack damage detection, namely YOLOv8-ES. We present a novel dynamic convolutional layer(EDCM) that successfully increases the feature extraction capabilities for small fractures.