• Convergence Security Journal
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• v.11 no.5
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• pp.99-108
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• 2011

The purpose of this study is to profile actual conditions of personal information protection systems operated in overseas countries and examine major considerations of personal information that security service providers must know in the capacity of privacy information processor, so that it may contribute to preventing potential occurrence of any legal disputes in advance. Particularly, this study further seeks to describe fundamental idea and principle of said Personal Information Protection Act; enhancement of various safety measures (e.g. collection / use of privacy data, processing of sensitive information / personal ID information, and encryption of privacy information); restrictions on installation / operation of video data processing devices; and penal regulations as a means of countermeasure against leakage of personal information, while proposing possible solutions to cope with these matters. Using cases among foreign countries for this study. Possible solutions proposed by this study can be summed up as follows: By changing minds with sufficient legal reviews, it is required for security service providers to 1) clearly and further specify any purposes of collecting and using privacy information, if possible, 2) obtain any privacy information by legitimate means as it is necessary to collect such information, 3) stop providing any personal information for the 3rd parties or for any other purposes except fundamental purposes of using privacy information, and 4) have full knowledge about duty of safety measure in accordance with safe maintenance of privacy information and protect any personal information from unwanted or intentional leakage to others.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.665-672
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• 2023

Purpose: Underground utility tunnel is facility that is jointly house infrastructure such as electricity, water and gas in city, causing condensation problems due to lack of airflow. This paper aims to prevent electricity leakage fires caused by condensation by detecting whether the control panel door in the underground utility tunnel is open using a deep learning model. Method: YOLO, a deep learning object recognition model, is trained to recognize the opening and closing of the control panel door using video data taken by a robot patrolling the underground utility tunnel. To improve the recognition rate, image augmentation is used. Result: Among the image enhancement techniques, we compared the performance of the YOLO model trained using mosaic with that of the YOLO model without mosaic, and found that the mosaic technique performed better. The mAP for all classes were 0.994, which is high evaluation result. Conclusion: It was able to detect the control panel even when there were lights off or other objects in the underground cavity. This allows you to effectively manage the underground utility tunnel and prevent disasters.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.41-51
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• 2024

In this paper, we explore the enhancement of target detection accuracy in the guided weapon using deep learning object detection on infrared (IR) images. Due to the characteristics of IR images being influenced by factors such as time and temperature, it's crucial to ensure a consistent representation of object features in various environments when training the model. A simple way to address this is by emphasizing the features of target objects and reducing noise within the infrared images through appropriate pre-processing techniques. However, in previous studies, there has not been sufficient discussion on pre-processing methods in learning deep learning models based on infrared images. In this paper, we aim to investigate the impact of image pre-processing techniques on infrared image-based training for object detection. To achieve this, we analyze the pre-processing results on infrared images that utilized global or local information from the video and the image. In addition, in order to confirm the impact of images converted by each pre-processing technique on object detector training, we learn the YOLOX target detector for images processed by various pre-processing methods and analyze them. In particular, the results of the experiments using the CLAHE (Contrast Limited Adaptive Histogram Equalization) shows the highest detection accuracy with a mean average precision (mAP) of 81.9%.