Texton Based Segmentation for Road Defect Detection from Aerial Imagery
Published: 5 January 2021
International Journal of Artificial Intelligence Research , Volume 4, pp 107-116; https://doi.org/10.29099/ijair.v4i2.179
Abstract: 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.
Keywords: survey / Aerial / Complaints / Texton / goods / Road Defect / segment the road
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