SmartDriveGuard: AI-Powered Real-Time Pothole Detection in Autonomous Vehicles Using YOLO-Based Object Detection

Ms. Roli Rai; Mr.Rajneesh Pachouri; Mr. Anurag Jain

SmartDriveGuard: AI-Powered Real-Time Pothole Detection in Autonomous Vehicles Using YOLO-Based Object Detection

Authors: Ms. Roli Rai, Mr.Rajneesh Pachouri, Mr. Anurag Jain

Unique Paper ID: 166839
Volume: 11
Issue: 2
PageNo: 2241-2248

Keywords: computer vision; real-time; pothole detection; deep learning; YOLO Neural Network real time detection Graphical User Interface.

Abstract:
Pothole detection is a critical component in maintaining road safety and minimizing vehicle damage. Traditional manual inspection methods are labor-intensive, time-consuming, and often subject to human error. 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. We evaluate the performance of different YOLO architectures, including YOLOv3, YOLOv4, and YOLOv5, on a dataset consisting of road images with and without potholes. The models are trained and tested to determine their accuracy, speed, and efficiency in identifying potholes under various lighting and weather conditions. The real-time detection capability is assessed using metrics such as frames per second (FPS) and average precision (AP). Our results indicate that YOLOv5 outperforms its predecessors in both accuracy and speed, making it the most suitable candidate for real-time applications. YOLOv4, while slightly less accurate, offers a good balance between detection speed and precision. YOLOv3, despite being the oldest model in the comparison, still provides reliable detection but falls short in real-time performance. The study highlights the potential of these models to be integrated into mobile or vehicular systems for continuous monitoring of road conditions. We discuss the implications of deploying these systems in smart city infrastructure, emphasizing the benefits of proactive maintenance and enhanced road safety. Future work will focus on optimizing these models for deployment on edge devices and improving their robustness against various environmental challenges.

Download article

email to a friend

Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 2
PageNo: 2241-2248

SmartDriveGuard: AI-Powered Real-Time Pothole Detection in Autonomous Vehicles Using YOLO-Based Object Detection

Available:https://ijirt.org/Article?manuscript=166839

23 Jul, 2024

Advancements in Parkinsonâ€™s disease Detection Using Automated Analysis of Spiral Drawings

22 Jul, 2024

Multimodal Feature Fusion with CNN-LSTM for Respiratory Disease Classification Using Lung Sound Analysis

19 Jul, 2024

Advanced Skin Disease Diagnosis Using VGG16 and CNN for Enhanced Accuracy

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

Join Our IPN

IJIRT Partner Network

Submit your research paper and those of your network (friends, colleagues, or peers) through your IPN account, and receive 800 INR for each paper that gets published.

Join Now

Latest Publication

Recent Conferences

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024 Last Date: 15th March 2024

Submit inquiry