Texton Based Segmentation for Road Defect Detection from Aerial Imagery

International Journal of artificial intelligence research

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Field Value
 
Title Texton Based Segmentation for Road Defect Detection from Aerial Imagery
 
Creator Prahara, Adhi
Akbar, Son Ali
Azhari, Ahmad
 
Subject

 
Description Road defect such as potholes and road cracks, became a problem that arose every year in Indonesia. It could endanger drivers and damage the vehicles. It also obstructed the goods distribution via land transportation that had major impact to the economy. To handle this problem, the government released an online complaints system that utilized information system and GPS technology. To follow up the complaints especially road defect problem, a survey was conducted to assess the damage. Manual survey became less effective for large road area and might disturb the traffic. Therefore, we used road aerial imagery captured by Unmanned Aerial Vehicle (UAV). The proposed method used texton combined with K-Nearest Neighbor (K-NN) to segment the road area and Support Vector Machine (SVM) to detect the road defect. Morphological operation followed by blob analysis was performed to locate, measure, and determine the type of defect. The experiment showed that the proposed method able to segment the road area and detect road defect from aerial imagery with good Boundary F1 score.
 
Publisher STMIK Dharma Wacana
 
Contributor
 
Date 2020-12-05
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://ijair.id/index.php/ijair/article/view/179
10.29099/ijair.v4i2.179
 
Source International Journal of Artificial Intelligence Research; Vol 4, No 2 (2020): December; 107 - 116
2579-7298
10.29099/ijair.v4i2
 
Language eng
 
Relation http://ijair.id/index.php/ijair/article/view/179/pdf
 
Rights Copyright (c) 2021 International Journal of Artificial Intelligence Research
https://creativecommons.org/licenses/by-sa/4.0
 

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