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Automated detection and classification of road infrastructure for intelligent urban planning.

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Bibliographic Details
Title: Automated detection and classification of road infrastructure for intelligent urban planning.
Authors: Bafail, Alaa, Aljeaid, Dania, Alotaibi, Hanan, Bajunaid, Noor, Algarni, Renad, Almutairi, Njoud, Alghamdi, Ebtsam
Source: Journal of Intelligent Systems; Jan2026, Vol. 35 Issue 1, p1-14, 14p
Subject Terms: DEEP learning, POTHOLES (Roads), URBAN planning, DETECTION algorithms, SUSTAINABLE transportation, REMOTE-sensing images, INFRASTRUCTURE (Economics)
Abstract: Asphalt roads play a vital role in land transportation systems, significantly contributing to the growth and development of economies and societies. However, over time, the quality of these roads deteriorates due to aging and the cumulative effects of wear and tear, leading to various pavement and road issues such as potholes, cracks, and damaged sidewalks. This paper aims to develop a deep learning model, specifically leveraging the YOLOv8 object detection framework, to detect and classify road infrastructure problems using images captured from unmanned aerial vehicles (UAVs). The model processes a series of road images from the Roboflow dataset and was trained on Google Colab, utilizing advanced machine learning techniques to analyze the images and accurately identify road damage. Subsequently, the model was evaluated using metrics such as accuracy, recall, precision, and F1-score. The results demonstrated that the model is both efficient and reliable. The model achieved high performance, with an F1-score of 94 %, precision of 93 %, and recall of 95 %, which indicates its effectiveness in identifying various road defects. By detecting and locating issues such as potholes, cracks, and sidewalk damage, this model offers a promising solution for maintaining road infrastructure, supporting smart transportation systems, enhancing road safety, and helping reduce hazards and accidents. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
Description
Abstract:Asphalt roads play a vital role in land transportation systems, significantly contributing to the growth and development of economies and societies. However, over time, the quality of these roads deteriorates due to aging and the cumulative effects of wear and tear, leading to various pavement and road issues such as potholes, cracks, and damaged sidewalks. This paper aims to develop a deep learning model, specifically leveraging the YOLOv8 object detection framework, to detect and classify road infrastructure problems using images captured from unmanned aerial vehicles (UAVs). The model processes a series of road images from the Roboflow dataset and was trained on Google Colab, utilizing advanced machine learning techniques to analyze the images and accurately identify road damage. Subsequently, the model was evaluated using metrics such as accuracy, recall, precision, and F1-score. The results demonstrated that the model is both efficient and reliable. The model achieved high performance, with an F1-score of 94 %, precision of 93 %, and recall of 95 %, which indicates its effectiveness in identifying various road defects. By detecting and locating issues such as potholes, cracks, and sidewalk damage, this model offers a promising solution for maintaining road infrastructure, supporting smart transportation systems, enhancing road safety, and helping reduce hazards and accidents. [ABSTRACT FROM AUTHOR]
ISSN:03341860
DOI:10.1515/jisys-2025-0055