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http://dx.doi.org/10.7236/IJIBC.2022.14.2.47

A study on road damage detection for safe driving of autonomous vehicles based on OpenCV and CNN

Lee, Sang-Hyun (Department of Computer Engineering, Honam University)

Publication Information

International Journal of Internet, Broadcasting and Communication / v.14, no.2, 2022 , pp. 47-54 More about this Journal

Abstract

For safe driving of autonomous vehicles, road damage detection is very important to lower the potential risk. In order to ensure safety while an autonomous vehicle is driving on the road, technology that can cope with various obstacles is required. Among them, technology that recognizes static obstacles such as poor road conditions as well as dynamic obstacles that may be encountered while driving, such as crosswalks, manholes, hollows, and speed bumps, is a priority. In this paper, we propose a method to extract similarity of images and find damaged road images using OpenCV image processing and CNN algorithm. To implement this, we trained a CNN model using 280 training datasheets and 70 test datasheets out of 350 image data. As a result of training, the object recognition processing speed and recognition speed of 100 images were tested, and the average processing speed was 45.9 ms, the average recognition speed was 66.78 ms, and the average object accuracy was 92%. In the future, it is expected that the driving safety of autonomous vehicles will be improved by using technology that detects road obstacles encountered while driving.

Keywords

OpenCV; CNN; Data augmentation; Histogram Equalization; Object accuracy;

Citations & Related Records

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