Real-Time Pothole Detection Using Convolutional Neural Networks and YOLOv8

Author(s)	Ms. Shiva Priya Thadakamalla, Prof. Dr. Suresh Babu K
Country	India
Abstract	Potholes are a major threat to road safety. They cause vehicle damage, accidents, and higher maintenance costs. Traditional methods for detecting potholes such as manual inspections or sensor-based systems, are often inefficient and resource-intensive. With the advancement of deep learning and computer vision, automated image-based detection systems have emerged as promising alternatives. This research builds on these advancements by proposing a hybrid approach combining image classification and object detection using ResNet50 and YOLOv8, aimed at improving the speed and accuracy of real-time pothole detection.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 16, Issue 3, July-September 2025
Published On	2025-07-08
DOI	https://doi.org/10.71097/IJSAT.v16.i3.6854
Short DOI	https://doi.org/g9sx6c

About IJSAT Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijsat.org

International Journal on Science and Technology