Current Volume 9
Abstract
Road safety systems need to do two things at once: spot hazards on the surface and track where the lane boundaries are. Most approaches handle one or the other. We built a system that does both. The framework pairs a YOLOv8m deep learning model for road damage detection with a classical computer vision pipeline for real-time lane tracking. Training followed a global-to-local strategy — we first trained on 6,832 images from India and Japan to build a broad foundation, then fine-tuned specifically on US road data. This transfer learning approach lets the model handle regional variation without starting from scratch for each country. For lane detection, we used Canny edge detection and Probabilistic Hough Transforms, constrained by a dynamic spatial mask that focuses the system on the relevant driving area. The result: a fine-tuned model with an mAP@0.5 of 0.571 across multiple road distress categories. A FastAPI backend ties both pipelines together, enabling concurrent damage detection and lane guidance in real time. Combining deep learning with geometric constraints gives autonomous systems a more complete picture of the road than either method could alone.
Citations
IRE Journals:
Madala jayanth Kumar, Patti Ebbeju, Maddineni Sai Ganesh, Kesana Pawan Chandhu "Yolo V8 And Canny: A Dual Detection System for Road Defect Analysis and Lane Guidance" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 3297-3301 https://doi.org/10.64388/IREV9I10-1717175
IEEE:
Madala jayanth Kumar, Patti Ebbeju, Maddineni Sai Ganesh, Kesana Pawan Chandhu
"Yolo V8 And Canny: A Dual Detection System for Road Defect Analysis and Lane Guidance" Iconic Research And Engineering Journals, 9(10) https://doi.org/10.64388/IREV9I10-1717175
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 8:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12