Deepbrokenhighways: Road Damage Recognition System Using Convolutional Neural Networks

Sebastian Peralta-Ireijo, Bill Chavez-Arias, Willy Ugarte

2024

Abstract

Road damage, such as potholes and cracks, represent a constant nuisance to drivers as they could potentially cause accidents and damages. Current pothole detection in Peru, is mostly manually operated and hardly ever use image processing technology. To combat this we propose a mobile application capable of real-time road damage detection and spatial mapping across a city. Three models are going to be trained and evaluated (Yolov5, Yolov8 and MobileNet v2) on a novel dataset which contains images from Lima, Peru. Meanwhile, the viability of crack detection through bounding box method will be put to the test, each model will be trained once with cracks annotations and without. The YOLOv5 model was the one with the best results, as it showed the best mAP50 across all of out experiments. It got 99.0% and 98.3% mAP50 with the dataset without crack and with crack annotations, correspondingly.

Paper Citation

in Harvard Style

Peralta-Ireijo S., Chavez-Arias B. and Ugarte W. (2024). Deepbrokenhighways: Road Damage Recognition System Using Convolutional Neural Networks. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 739-746. DOI: 10.5220/0012685600003690

in Bibtex Style

@conference{iceis24,
author={Sebastian Peralta-Ireijo and Bill Chavez-Arias and Willy Ugarte},
title={Deepbrokenhighways: Road Damage Recognition System Using Convolutional Neural Networks},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={739-746},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012685600003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Deepbrokenhighways: Road Damage Recognition System Using Convolutional Neural Networks
SN - 978-989-758-692-7
AU - Peralta-Ireijo S.
AU - Chavez-Arias B.
AU - Ugarte W.
PY - 2024
SP - 739
EP - 746
DO - 10.5220/0012685600003690
PB - SciTePress