Investigation on a Vision-Based Approch For Smart Pothole Detection Using Deep learning Based on Fast CNN

Authors

  • Ashraf Khaled Alsharafi Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pahang, Malaysia.
  • Muhammed Nafis Osman Zahid Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pahang, Malaysia.

DOI:

https://doi.org/10.15282/mekatronika.v5i2.9813

Keywords:

Yolo models, Mean Average Precision, Smart Pothole Detection, Vision-Based Approch

Abstract

The quality of road these days are important and roads always dangerous since its filled with potholes and damages which cause a lot of incident and numbers gets more increased in crowded area , this article investigates and compare the performance metrics of different object detection models that utilized the Fast CNN structure in it's backbones , Four processes make up the standard method of pothole detection: data acquisition, data pre-processing, feature extraction, and pothole classification. for the task of pothole detection. The study focuses on the evaluation of YOLOv6n, YOLOv8n, YOLOv5n, and YOLOv7 models using a dataset of road images containing pothole instances. The performance metrics analyzed include precision (P), recall (R), mean average precision at 50% IoU (mAP@.5), and mean average precision from 50% to 95% IoU (mAP@.5:.95) . The findings indicate that YOLOv8n demonstrates the highest overall performance, achieving significant precision and recall rates. These results provide valuable insights into the effectiveness of object detection models for pothole detection, contributing to the field of road maintenance and safety. The outcomes of this study can assist in the development of intelligent systems for automated pothole detection and maintenance planning

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Published

2023-12-17

How to Cite

[1]
A. K. Alsharafi and M. N. Osman Zahid, “Investigation on a Vision-Based Approch For Smart Pothole Detection Using Deep learning Based on Fast CNN”, MEKATRONIKA, vol. 5, no. 2, pp. 87–99, Dec. 2023.

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Section

Original Article