Abstract
Pothole repair is one of the paramount tasks in road maintenance. Effective road surface monitoring is an ongoing challenge to the management agency. The current pothole detection, which is conducted image processing with a manual operation, is labour-intensive and time-consuming. Computer vision offers a mean to automate its visual inspection process using digital imaging, hence, identifying potholes from a series of images. The goal of this study is to apply different YOLO models for pothole detection. Three state-of-the-art object detection frameworks (i.e., YOLOv4, YOLOv4-tiny, and YOLOv5s) are experimented to measure their performance involved in real-time responsiveness and detection accuracy using the image set. The image set is identified by running the deep convolutional neural network (CNN) on several deep learning pothole detectors. After collecting a set of 665 images in 720 × 720 pixels resolution that captures various types of potholes on different road surface conditions, the set is divided into training, testing, and validation subsets. A mean average precision at 50% Intersection-over-Union threshold (mAP_0.5) is used to measure the performance of models. The study result shows that the mAP_0.5 of YOLOv4, YOLOv4-tiny, and YOLOv5s are 77.7%, 78.7%, and 74.8%, respectively. It confirms that the YOLOv4-tiny is the best fit model for pothole detection.
Highlights
Road maintenance conditions are an important factor contributing to decrease the probability of accidents on road. 94% of road accidents are attributed to poor maintenance conditions in USA [1]
The main contribution of this paper is to find out the best fit model that allows efficiently heterogenous damage detection on the surface of road
A pothole dataset was collected from the previous research and the various You Only Look Once (YOLO) models were reconstructed to be suitable for the tasks of pothole detection
Summary
Road maintenance conditions are an important factor contributing to decrease the probability of accidents on road. 94% of road accidents are attributed to poor maintenance conditions in USA [1]. Road maintenance conditions are an important factor contributing to decrease the probability of accidents on road. 94% of road accidents are attributed to poor maintenance conditions in USA [1]. It is well accepted that the methods which may improve road maintenance performance is important to reduce the occurrence of accidents. The surface quality of road, which is a dominant factor of road conditions, is performed manual base, being costly and time-consuming. Existing methods need involvement by expert(s) to determine the surface condition associated with roads. The state of art in visual detection along with electronic devices provide a measure to get images associated with the surface quality of road with affordable cost automatically. Existing studies detect all kinds of nonconformity on traffic roads by obtaining clear images of wear, tear and damage being in various severity
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
