• Journal of the Korea Computer Graphics Society
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• v.28 no.4
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• pp.23-30
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• 2022

In this paper, we propose an algorithm that can accurately segment a fire even when it is surrounded by smoke of a similar color. Existing fire area segmentation algorithms have a problem in that they cannot separate fire and smoke from fire images. In this paper, the fire was successfully separated from the smoke by applying the color compensation method and the fog removal method as a preprocessing process before applying the fire area segmentation algorithm. In fact, it was confirmed that it segments fire more effectively than the existing methods in the image of the fire scene covered with smoke. In addition, we propose a method that can use the proposed fire segmentation algorithm for efficient fire detection in factories and homes.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.9-12
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• 2019

지난 수 년 동안 계속해서 일반 실상 카메라를 이용한 영상분석기술에 대한 연구가 활발히 진행되고 있다. 최근에는 딥러닝 기술을 적용한 지능형 영상분석기술로 발전해 왔으며 국방기지방호, CCTV, 사용자 얼굴인식, 머신비전, 자동차, 드론 산업이 활성화되면서 많은 시너지를 효과를 일으키고 있다. 그러나 어두운 밤과 안개, 날씨, 연기 등 다양한 여건에서 따라서 카메라의 영상분석 정확성 감소와 오류가 수반될 수 있으며 일반적으로 딥러닝 기술을 활용하기 위해서는 고사양의 GPU를 필요로 하기 때문에 다른 추가적인 시스템이 요구된다. 이에 본 연구에서는 열적외선 영상의 객체 검출에 적용하기 위해 SSD(Single Shot MultiBox Detector) 기반의 경량적인 MobilNet 네트워크로 재구성하여, 모바일 기기 등 낮은 사양의 낮은 임베디드 시스템에서도 활용 할 수 있는 방법을 제안한다. 모의 실험결과 제안된 방식의 모델은 적외선 열화상 카메라에서 객체검출과 학습시간이 줄어든 것을 확인 할 수 있었다.

• The KIPS Transactions:PartB
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• v.14B no.6
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• pp.445-452
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• 2007

We proposed a detection scheme of weather information in CCTV video images in this paper. The scheme obtains the RGB distribution of shiny day and divide a target image into cloud, rain, snow and for RGB distributions. shiny day RGB distribution. Our scheme designed systematically to detection and separation special characteristics of images from complex weather information. Our algorithm has less overhead than the previous methods to use weather database DB at the view of time and space. And our algorithm can be use in real world system with low cost of implementation. Also, our algorithm use informations of temperature, humidity, date, and time to detect the information of weather with high quality.

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.839-842
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• 2009

It is used to differential image for moving object detection in general. But it is difficult to detect the accurate detection which uses differential image between frames. In this paper, the proposed method overcome the noise that is generated by camera, grabber card, or weather condition. It extract to moving big object such as human or vehicle. The proposed method process morphological filtering and binary for the image with noise, reduce error. We are expect to apply to a real-time moving object detection system at fog condition, pass the limit of the object detection method using the differential image.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1257-1262
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• 2018

In this paper, a deep learning method is proposed to detect the fire effectively by using video of surveillance camera. Based on AlexNet model, classification performance is compared according to kernel size and stride of convolution layer. Dataset for learning and interfering are classified into two classes such as normal and fire. Normal images include clouds, and foggy images, and fire images include smoke and flames images, respectively. As results of simulations, it is shown that the larger kernel size and smaller stride shows better performance.

• Journal of IKEEE
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• v.18 no.4
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• pp.485-494
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• 2014

Traffic Sign Recognition(TSR) is an important element in an Advanced Driver Assistance System(ADAS). However, many studies related to TSR approaches only in normal daytime environment because a sign's unique color doesn't appear in poor environment such as night time, snow, rain or fog. In this paper, we propose a new TSR algorithm based on machine learning for daytime as well as poor environment. In poor environment, traditional methods which use RGB color region doesn't show good performance. So we extracted sign characteristics using HoG extraction, and detected signs using a Support Vector Machine(SVM). The detected sign is recognized by a decision tree based on 25 reference points in a Normalized RGB system. The detection rate of the proposed system is 96.4% and the recognition rate is 94% when applied in poor environment. The testing was performed on an Intel i5 processor at 3.4 GHz using Full HD resolution images. As a result, the proposed algorithm shows that machine learning based detection and recognition methods can efficiently be used for TSR algorithm even in poor driving environment.

• Aerospace Engineering and Technology
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• v.3 no.1
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• pp.242-250
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• 2004

This paper describes the detection method of wire-like obstacles using millimeter-wave radar system. Passive sensor like CCD camera can be used for the detection of high power electric cables on the hills or mountains and it can give very good quality of obstacle target information. But this system is very limited to use by bad weather condition. The detection capability for different diameters of wire targets using millimeter radar system have been accomplished. To simulate the target on the moving helicopter, rotating targets are used with fixed radar system. In the experiment 11mm, 16mm and 22mm diameter of wires have been detected in single, two and three wires in one position. The detected signal from single wire was very clear on gray level image. Three wires placed very closely together could be recognized in range, cross range image plane. For two and three wires, blur effect due to mutual scattering effect is observed.

• Journal of Korea Multimedia Society
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• v.10 no.2
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• pp.209-217
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• 2007

This paper proposed to a detection scheme of weather information that is a part of CCTV Images Weather Detection System using CCTV images and Temperature, Humidity. The previous Partial Weather Detection System uses how to acquire weather information using images on the Road. In the system the contrast and RGB Values using clear images are gained. This information is distributed a input images to cloud, rain, snow and fog images. That is, this information is compared the snow and the fog images for acquisition more correctness information us ing difference images and binary images. Currently, We use to environment sense system, but we suggest a new Weather Detection Algorithm to detect weather information using CCTV images. Our algorithm is designed simply and systematically to detect and separate special characteristics of images from CCTV images. and using temperature & humidity in formation. This algorithm, there is more complex to implement than how to use DB with high overhead of time and space in the previous system. But our algorithm can be implement with low cost' and can be use the system in real work right away. Also, our algorithm can detect the exact information of weather with adding in formation including temperature, humidity, date, and time. At last, this paper s how the usefulness of our algorithm.

• Journal of the Semiconductor & Display Technology
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• v.17 no.2
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• pp.76-80
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• 2018

In recent years, with the development of automobile technology, smart system technology that assists safe driving has been developed. A camera is installed on the front and rear of the vehicle as well as on the left and right sides to detect and warn of collision risks and hazards. Beyond the technology of simple black-box recording via cameras, we are developing intelligent systems that combine various computer vision technologies. However, most related studies have been developed to optimize performance in laboratory-like environments that do not take environmental factors such as weather into account. In this paper, we propose a method to detect object by restoring visibility in image with degraded image due to weather factors such as fog. First, the image quality degradation such as fog is detected in a single image, and the image quality is improved by restoring using an intermediate value filter. Then, we used an adaptive feature extraction method that removes unnecessary elements such as noise from the improved image and uses it to recognize objects with only the necessary features. In the proposed method, it is shown that more feature points are extracted than the feature points of the region of interest in the improved image.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1023-1030
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• 2023

FMCW LiDAR system with robust target detection capabilities even under adverse operating conditions such as snow, rain, and fog is addressed in this paper. Our focus is primarily on enhancing the performance of FMCW LiDAR by improving the characteristics of the frequency-modulated laser, which directly influence range resolution, coherence length, and maximum measurement range etc. of LiDAR. We describe the utilization of an unbalanced Mach-Zehnder laser interferometer to measure real-time changes of the lasing frequency and to correct frequency modulation errors through an optical phase-locked loop technique. To extend the coherence length of laser, we employ an extended-cavity laser diode as the laser source and implement a laser interferometer with an photonic integrated circuit for miniaturization of optical system. The developed FMCW LiDAR system exhibits a bandwidth of 10.045GHz and a remarkable distance resolution of 0.84mm.