article · Frontiers in Built Environment
Autonomous vehicles require reliable methods to identify road hazards such as potholes to prevent vehicular damage and safeguard passengers. To address this challenge, a deep learning detection system using the YOLOv8 object detection algorithm was developed for real-time pothole recognition. The system aims to allow self-driving vehicles to identify and evade hazards, thereby reducing accident risks on complex roads. Tested against publicly available benchmark datasets, the model surpassed existing state-of-the-art techniques, including YOLOv5, in terms of both detection accuracy and processing efficiency. Additionally, multiple data augmentation techniques were examined to enhance the system detection capabilities. The outcomes show that YOLOv8 provides an effective mechanism to improve the road safety and operational reliability of autonomous travel.
Potholes create major safety hazards for passengers and cause severe mechanical damage to road vehicles. Creating accurate, real-time computer vision tools allows autonomous driving systems to detect and navigate around road defects safely, which is critical for reducing accidents and ensuring the dependable deployment of self-driving transport across everyday road networks.
This software solution is designed for integration into autonomous vehicle navigation and safety systems to facilitate real-time hazard avoidance. The primary prospective users are self-driving vehicle manufacturers and automotive software developers. Because the system has been tested and benchmarked against public datasets rather than in live automotive fleet trials, it currently sits at an applied and tested algorithmic stage.
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Autonomous vehicles can transform the transportation sector by offering a safer and more effective means of travel. However, the success of self-driving cars depends on their ability to navigate complex road conditions, including the detection of potholes. Potholes pose a substantial risk to vehicles and passengers, leading to potential damage and safety hazards, making their detection a critical task for autonomous driving. In this work, we propose a robust and efficient solution for pothole detection using the “you look only once (YOLO) algorithm of version 8, the newest deep learning object detection algorithm.” Our proposed system employs a deep learning methodology to identify real-time potholes, enabling autonomous vehicles to avoid potential hazards and minimise accident risk. We assess the effectiveness of our system using publicly available datasets and show that it outperforms existing state-of-the-art approaches in terms of accuracy and efficiency. Additionally, we investigate different data augmentation methods to enhance the detection capabilities of our proposed system. Our results demonstrate that YOLO V8-based pothole detection is a promising solution for autonomous driving and can significantly improve the safety and reliability of self-driving vehicles on the road. The results of our study are also compared with the results of YOLO V5.
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DOI: 10.3389/fbuil.2023.1323792
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