Road Detection And Mapping Using The Android Platform Integrated With Raspberry Pi Based On Computer Vision

Chandra, Andreas Peter (2022) Road Detection And Mapping Using The Android Platform Integrated With Raspberry Pi Based On Computer Vision. Undergraduate thesis, Universitas 17 Agustus 1945 Surabaya.

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Abstract

The importance of highways in supporting community activities should be regularly maintained along with the increase in road users. Unfortunately, there are still many potholes that are a problem for paved roads. Based on data from the Central Statistics Agency, the recorded road data can be categorized according to the damaged road condition, namely 14.98% with a damaged state and 20.47% with heavily damaged conditions. One of the reasons for the slow repair of roads is because the data recording of potholes still uses manual techniques that require human labor, so the process takes longer. This study aims to detect potholes on highways automatically. It utilizes computer vision technology to use the YOLO method as digital image processing to identify pothole objects. It has a total training dataset of 300 images with a final accuracy rate of 96.9% carried out in 7500 iterations and produces an average loss of 0.2132. The test gets the lowest confidence value of 0.30 and the highest value is 0.93. Tests were carried out to determine the variables that affected the trial. The data obtained can be accessed in the Android platform application to determine the position of the potholes. Keywords: Potholes, Computer Vision, YOLO, Digital Image Processing

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