• Journal of the Korea Society of Computer and Information
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• v.28 no.5
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• pp.17-28
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• 2023

Potholes that occur on paved roads can have fatal consequences for vehicles traveling at high speeds and may even lead to fatalities. While manual detection of potholes using human labor is commonly used to prevent pothole-related accidents, it is economically and temporally inefficient due to the exposure of workers on the road and the difficulty in predicting potholes in certain categories. Therefore, completely preventing potholes is nearly impossible, and even preventing their formation is limited due to the influence of ground conditions closely related to road environments. Additionally, labeling work guided by experts is required for dataset construction. Thus, in this paper, we utilized the Mean Teacher technique, one of the semi-supervised learning-based knowledge distillation methods, to achieve robust performance in pothole image classification even with limited labeled data. We demonstrated this using performance metrics and GradCAM, showing that when using semi-supervised learning, 15 pre-trained CNN models achieved an average accuracy of 90.41%, with a minimum of 2% and a maximum of 9% performance difference compared to supervised learning.

• Journal of the Korea Institute of Military Science and Technology
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• v.18 no.6
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• pp.711-720
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• 2015

Recently many studies of Radar systems mounted on ground vehicles for autonomous driving, SLAM (Simultaneous localization and mapping) and collision avoidance have been reported. Since several pixels per an object may be generated in a close-range radar application, a width of an object can be estimated automatically by various signal processing techniques. In this paper, we tried to attempt to develop an algorithm to estimate obstacle width using Radar images. The proposed method consists of 5 steps - 1) background clutter reduction, 2) local peak pixel detection, 3) region growing, 4) contour extraction and 5)width calculation. For the performance validation of our method, we performed the test width estimation using a real data of two cars acquired by commercial radar system - I200 manufactured by Navtech. As a result, we verified that the proposed method can estimate the widths of targets.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.2
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• pp.342-348
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• 2016

We propose an obstacle detection algorithm for unmanned ground vehicle on uneven terrain. The key ideas of the proposed algorithm are the use of two-layer laser range data to calculate the gradient of a target, which is characterized as either ground or obstacles. The proposed obstacle detection algorithm includes 4-steps: 1) Obtain the distance data for each angle from multiple lidars or a multi-layer scan lidar. 2) Calcualate the gradient for each angle of the uneven terrain. 3) Determine ground or obstacle for each angle on the basis of reference gradient. 4) Generate a new distance data for each angle for a virtual laser scanner. The proposed algorithm is verified by various experiments.

• Journal of Satellite, Information and Communications
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• v.7 no.1
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• pp.75-81
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• 2012

This study is to design the transmitter and receiver of short range UWB(Ultra Wide Band) imaging radar that is able to display high resolution radar image for front area of a UGV(Unmanned Ground Vehicle). This radar can help a UGV to navigate autonomously as it detects and avoids obstacles through foliage. The transmitter needs two transmitters to improve the azimuth resolution. Multi-channel receivers are required to synthesize radar image. Transmitter consists of high power amplifier, channel selection switch, and waveform generator. Receiver is composed of sixteen channel receivers, receiver channel converter, and frequency down converter, Before manufacturing it, the proposed architecture of transceiver is proved by modeling and simulation using several parameters. Then, it was manufactured by using industrial RF(Radio Frequency) components and all other measured parameters in the specification were satisfied as well.