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A Semantic Segmentation Approach for Road Defect Detection and Quantification

Published: 02 May 2022 Publication History

Abstract

Automated visual detection and quantification of road defects has been a hot research topic for quite a long time due to its practical importance for road maintenance and traffic safety. However, uncertainties associated with the 2D images, such as non-uniformity of defects, insufficient background illumination, and etc., make it a challenging problem. This research work aims to solve the problem by employing a deep learning based approach. Specifically, image segmentation has been carried out, using a convolutional encoder-decoder model, to segment various defects from the non-defect area of the road. The method lead to a reasonable segmentation of different defects. Consequently, the extracted defect areas, in terms of number of pixels, are used to derive road condition indices being followed in Germany. In comparison, the indices derived using deep learning based approach are found to more accurate than those derived using conventional approach.

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Cited By

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  • (2024)Computer Vision-based road surveillance system using autonomous drones and sensor fusion2024 27th International Conference on Information Fusion (FUSION)10.23919/FUSION59988.2024.10706468(1-7)Online publication date: 8-Jul-2024
  • (2023)A Semantic Segmentation Method of Front-View Pavement Distress Based on SegFormer2023 4th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE)10.1109/ICBASE59196.2023.10303085(284-288)Online publication date: 25-Aug-2023

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            cover image ACM Other conferences
            ICMVA '22: Proceedings of the 2022 5th International Conference on Machine Vision and Applications
            February 2022
            128 pages
            ISBN:9781450395670
            DOI:10.1145/3523111
            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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            Published: 02 May 2022

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            Author Tags

            1. computer vision
            2. machine learning
            3. neural network
            4. street quality estimation

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            • (2024)Computer Vision-based road surveillance system using autonomous drones and sensor fusion2024 27th International Conference on Information Fusion (FUSION)10.23919/FUSION59988.2024.10706468(1-7)Online publication date: 8-Jul-2024
            • (2023)A Semantic Segmentation Method of Front-View Pavement Distress Based on SegFormer2023 4th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE)10.1109/ICBASE59196.2023.10303085(284-288)Online publication date: 25-Aug-2023

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