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자동차 안전운전 보조 시스템에 응용할 수 있는 카메라 캘리브레이션 방법
박종섭,김기석,노수장,조재수,Park, Jong-Seop,Kim, Gi-Seok,Roh, Soo-Jang,Cho, Jae-Soo 제어로봇시스템학회 2015 제어·로봇·시스템학회 논문지 Vol.21 No.7
This paper presents a camera calibration method in order to estimate the lane detection and inter-vehicle distance estimation system for an automotive safety driving system. In order to implement the lane detection and vision-based inter-vehicle distance estimation to the embedded navigations or black box systems, it is necessary to consider the computation time and algorithm complexity. The process of camera calibration estimates the horizon, the position of the car's hood and the lane width for extraction of region of interest (ROI) from input image sequences. The precision of the calibration method is very important to the lane detection and inter-vehicle distance estimation. The proposed calibration method consists of three main steps: 1) horizon area determination; 2) estimation of the car's hood area; and 3) estimation of initial lane width. Various experimental results show the effectiveness of the proposed method.
자동차 안전운전 보조 시스템에 응용할 수 있는 카메라 캘리브레이션 방법
박종섭(Jong-Seop Park),김기석(Gi-Seok Kim),노수장(Soo-Jang Roh),조재수(Jae-Soo Cho) 제어로봇시스템학회 2015 제어·로봇·시스템학회 논문지 Vol.18 No.1
This paper presents a camera calibration method in order to estimate the lane detection and inter-vehicle distance estimation system for an automotive safety driving system. In order to implement the lane detection and vision-based inter-vehicle distance estimation to the embedded navigations or black box systems, it is necessary to consider the computation time and algorithm complexity. The process of camera calibration estimates the horizon, the position of the car’s hood and the lane width for extraction of region of interest (ROI) from input image sequences. The precision of the calibration method is very important to the lane detection and inter-vehicle distance estimation. The proposed calibration method consists of three main steps: 1) horizon area determination; 2) estimation of the car’s hood area; and 3) estimation of initial lane width. Various experimental results show the effectiveness of the proposed method.
안드로이드 리패키징(Repackaging) 탐지 기술 설계 및 구현
박종섭 ( Jong-seop Park ),박상호 ( Sang-ho Park ),박찬암 ( Chanam Park ),이종호 ( Jong-ho Lee ),신동휘 ( Donghwi Shin ) 한국정보처리학회 2012 한국정보처리학회 학술대회논문집 Vol.19 No.1
스마트폰 사용이 급증하고 있는 현재, 안드로이드 OS 기반 스마트폰 점유율이 가장 큰 상승세를 보이고 있다. 하지만 안드로이드 OS는 자가-서명 인증서(self-signed certificate)로 애플리케이션을 검증하여, 많은 보안상의 취약점을 내재하고 있다. 자가-서명 인증서의 검증 취약점을 이용하여, 악의적인 공격자는 기존 정상 애플리케이션에 악성코드를 삽입, 리패키징(Repackaging) 하여 마켓에 유포할 수 있다. 이러한 문제를 해결하기 위해서, 본 논문에서는 안드로이드 애플리케이션의 서명 파일을 이용한 애플리케이션 리패키징 여부를 탐지하는 기술을 설계 및 구현한다.
ICT 기술을 융합한 자동차 실러도포 공정 모니터링 시스템
김호연(Ho Yeon Kim),박종섭(Jong Seop Park),박요한(Yo Han Park),조재수(Jae Soo Cho) 한국IT서비스학회 2018 한국IT서비스학회지 Vol.17 No.3
In this paper, we propose a car sealing monitoring system combined with ICT Technology. The automobile sealer is an adhesive used to bond inner and outer panels of doors, hoods and trunks of an automobile body. The proposed car sealer monitoring system is a system that can accurately and automatically inspect the condition of the automobile sealer coating process in the general often factory production line where the lighting change is very severe. The sealer inspection module checks the state of the applied sealer using an area scan camera. The vision inspection algorithm is adaptive to various lighting environments to determine whether the sealer is defective or not. The captured images and test results are configured to send the task results to the task manager in real-time as a smartphone app. Vision inspection algorithms in the plant outdoors are very vulnerable to time-varying external light sources and by configuring a monitoring system based on smart mobile equipment, it is possible to perform production monitoring regardless of time and place. The applicability of this method was verified by applying it to an actual automotive sealer application process.
듀얼 모드(고정형+PTZ 카메라) 감시 카메라를 이용한 효과적인 화상 감시 시스템에 관한 연구
김기석(Giseok Kim),이삭(Saac Lee),박종섭(Jong-Seop Park),조재수(Jae-Soo Cho) 제어로봇시스템학회 2012 제어·로봇·시스템학회 논문지 Vol.18 No.7
An effective dual-mode camera system(a passive wide-angle camera and a pan-tilt-zoom camera) is proposed in order to improve the performance of visual surveillance. The fixed wide-angle camera is used to monitor large open areas, but the moving objects on the images are too small to view in detail. And, the PTZ camera is capable of increasing the monitoring area and enhancing the image quality by tracking and zooming in on a specific moving target. However, its FOV (Field of View) is limited when zooming in on a specific target. Therefore, the cooperation of wide-angle and PTZ cameras is complementary. In this paper, we propose an automatic initial set-up algorithm and coordinate transform method from the wide-angle camera coordinate to the PTZ one, which are necessary to achieve the cooperation. The automatic initial set-up algorithm is able to synchronize the views of two cameras. When a moving object appears on the image plane of a wide-angle camera after the initial set-up positioning, the obtained values of the wide-angle camera should be transformed to the PTZ values based on the coordinate transform method. We also develope the PTZ control method. Various in-door and out-door experiments show that the proposed dual-camera system is feasible for the effective visual surveillance.
조명의 변화가 심한 환경에서 자동차 부품 유무 비전검사 방법
김기석(Giseok Kim),박요한(Yo Han Park),박종섭(Jong-Seop Park),조재수(Jae-Soo Cho) 제어로봇시스템학회 2015 제어·로봇·시스템학회 논문지 Vol.21 No.12
This paper presents an improved learning-based visual inspection method for auto parts inspection in severe lighting changes. Automobile sunroof frames are produced automatically by robots in most production lines. In the sunroof frame manufacturing process, there is a quality problem with some parts such as volts are missed. Instead of manual sampling inspection using some mechanical jig instruments, a learning-based machine vision system was proposed in the previous research[1]. But, in applying the actual sunroof frame production process, the inspection accuracy of the proposed vision system is much lowered because of severe illumination changes. In order to overcome this capricious environment, some selective feature vectors and cascade classifiers are used for each auto parts. And we are able to improve the inspection accuracy through the re-learning concept for the misclassified data. The effectiveness of the proposed visual inspection method is verified through sufficient experiments in a real sunroof production line.