Deepbrokenhighways: Road Damage Recognition System Using Convolutional Neural Networks

• Autores: Peralta-Ireijo, Sebastian, Chavez-Arias, Bill, Ugarte, Willy
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/676063
• Enlace del recurso: http://hdl.handle.net/10757/676063
• Nivel de acceso: acceso abierto
• Materia: Computer Vision; Convolutional Neural Network; MobileNet; Pothole Detection; YOLO

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..