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Investigation on a vision-based approch for smart pothole detection using deep learning based on fast CNN

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 u...

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Main Authors: Alsharafi, Ashraf Khaled, Muhammed Nafis, Osman Zahid
Format: Article
Language:English
Published: Penerbit UMP 2023
Subjects:
TJ Mechanical engineering and machinery
Online Access:http://umpir.ump.edu.my/id/eprint/40089/1/Investigation%20on%20a%20Vision-Based%20Approch%20For%20Smart%20Pothole%20Detection.pdf
http://umpir.ump.edu.my/id/eprint/40089/
https://doi.org/10.15282/mekatronika.v5i2.9813
https://doi.org/10.15282/mekatronika.v5i2.9813
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spelling my.ump.umpir.400892024-01-18T07:08:35Z http://umpir.ump.edu.my/id/eprint/40089/ Investigation on a vision-based approch for smart pothole detection using deep learning based on fast CNN Alsharafi, Ashraf Khaled Muhammed Nafis, Osman Zahid TJ Mechanical engineering and machinery 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 Penerbit UMP 2023-07 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/40089/1/Investigation%20on%20a%20Vision-Based%20Approch%20For%20Smart%20Pothole%20Detection.pdf Alsharafi, Ashraf Khaled and Muhammed Nafis, Osman Zahid (2023) Investigation on a vision-based approch for smart pothole detection using deep learning based on fast CNN. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 5 (2). pp. 87-99. ISSN 2637-0883. (Published) https://doi.org/10.15282/mekatronika.v5i2.9813 https://doi.org/10.15282/mekatronika.v5i2.9813
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Alsharafi, Ashraf Khaled
Muhammed Nafis, Osman Zahid
Investigation on a vision-based approch for smart pothole detection using deep learning based on fast CNN
description 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
format Article
author Alsharafi, Ashraf Khaled
Muhammed Nafis, Osman Zahid
author_facet Alsharafi, Ashraf Khaled
Muhammed Nafis, Osman Zahid
author_sort Alsharafi, Ashraf Khaled
title Investigation on a vision-based approch for smart pothole detection using deep learning based on fast CNN
title_short Investigation on a vision-based approch for smart pothole detection using deep learning based on fast CNN
title_full Investigation on a vision-based approch for smart pothole detection using deep learning based on fast CNN
title_fullStr Investigation on a vision-based approch for smart pothole detection using deep learning based on fast CNN
title_full_unstemmed Investigation on a vision-based approch for smart pothole detection using deep learning based on fast CNN
title_sort investigation on a vision-based approch for smart pothole detection using deep learning based on fast cnn
publisher Penerbit UMP
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/40089/1/Investigation%20on%20a%20Vision-Based%20Approch%20For%20Smart%20Pothole%20Detection.pdf
http://umpir.ump.edu.my/id/eprint/40089/
https://doi.org/10.15282/mekatronika.v5i2.9813
https://doi.org/10.15282/mekatronika.v5i2.9813
_version_ 1822924096528187392
score 13.232414

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