• Title/Summary/Keyword: Vision data

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Forest Fire Detection System using Drone Streaming Images (드론 스트리밍 영상 이미지 분석을 통한 실시간 산불 탐지 시스템)

  • Yoosin Kim
    • Journal of Advanced Navigation Technology
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    • v.27 no.5
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    • pp.685-689
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    • 2023
  • The proposed system in the study aims to detect forest fires in real-time stream data received from the drone-camera. Recently, the number of wildfires has been increasing, and also the large scaled wildfires are frequent more and more. In order to prevent forest fire damage, many experiments using the drone camera and vision analysis are actively conducted, however there were many challenges, such as network speed, pre-processing, and model performance, to detect forest fires from real-time streaming data of the flying drone. Therefore, this study applied image data processing works to capture five good image frames for vision analysis from whole streaming data and then developed the object detection model based on YOLO_v2. As the result, the classification model performance of forest fire images reached upto 93% of accuracy, and the field test for the model verification detected the forest fire with about 70% accuracy.

The Influence of the Reflected Arc Light on Vision Sensors for Welding Process Autimation (물체의 반사성질이 용접자동화용 시각센서의 아크노이즈에 미치는 영향에 관한 연구)

  • 이철원;나석주
    • Journal of Welding and Joining
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    • v.13 no.1
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    • pp.115-126
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    • 1995
  • Vision sensors using the optical triangulation have been widely used for automatic welding systems in various ways, but their reliability is seriously affected by presence of the arc noise. The reliability of vision sensors was analyzed with variation of the arc noise by considering the reflectance of the base metal. first, the properties of the base metal's reflection were modelled by using the Bidirectional Reflectance-Distribution Function(BRDF), and then the variation of the reflected arc intensity was formulated for various configurations of the torch, base metal, and sensor. The experimental data of the gray level of the reflected arc light were obtained for two materials, mild steel and stainless steel. It was found that the results calculated from the proposed model were in good agreement with the experimental data.

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Inspection for Large 2D machining product using robot vision (로봇비젼을 이용한 대형 2차원 가공물의 검사)

  • 정병묵;이성건;조지승
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2002.05a
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    • pp.177-180
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    • 2002
  • Generally, it is very difficult to inspect geometric shape of large 2D objects after machining. To maintain the accuracy for inspection, a robot vision is used to divide overall shape into several enlarged images, and image processing technique is applied to acquire one minute geometric contour. The inspection is to compare the NC data with the measured contour data by the vision system, and the algorithm is to rotate to minimize the maximum deviation coinciding two geometric centers. This paper experimentally shows that the proposed inspection algorithm is very useful fur a large machined object.

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A Study on Vision Sensor-based Measurement of Die Location for Its Remodeling (금형 개조 용접시 시각 센서를 이용한 대상물 위치 파악에 관한 연구)

  • Kim, Jitae;Na, Suck-Joo
    • Journal of the Korean Society for Precision Engineering
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    • v.17 no.10
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    • pp.141-146
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    • 2000
  • We introduce the algorithms of 3-D position estimation using a laser sensor for automatic die remodeling. First, a vision sensor based on the optical triangulation was used to collect the range data of die surface. Second, line vector equations were constructed by the measured range data, and an analytic algorithm was proposed for recognizing the die location with these vector equations. This algorithm could make the transformation matrix without any specific corresponding points. To ascertain this algorithm, folded SUS plate was measured by the laser vision sensor attached to a 3-axis cartesian manipulator and the transformation matrix was calculated.

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Vision Inspection for Large 2D Machining Product using Tolerance Zone (공차영역을 이용한 대형 2차원 가공물의 형상 검사)

  • 이성건;정병묵;조지승
    • Journal of the Korean Society for Precision Engineering
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    • v.19 no.11
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    • pp.112-119
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    • 2002
  • Generally, it is very difficult to inspect geometric shape of large 2D objects after machining. To maintain the accuracy for inspection, a robot vision is used to divide overall shape into several enlarged images, and image processing technique is applied to acquire one minute geometric contour. The inspection is to compare the NC data with the measured contour data by the vision system, and the algorithm is to rotate to minimize the maximum deviation after coinciding two geometric centers. This paper experimentally shows that the proposed algorithm is very useful for inspection of large machined objects.

Automatic Recognition of In-Process mold Dies Based on Reverse Engineering Technology (형상 역공학을 통한 공정중 금형 가공물의 자동인식)

  • 김정권;윤길상;최진화;김동우;조명우;박균명
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 2003.10a
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    • pp.420-425
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    • 2003
  • Generally, reverse engineering means getting CAD data from unidentified shape using vision or 3D laser scanner system. In this paper, we studied unidentified model by machine vision based reverse engineering system to get information about in-processing model. Recently, vision technology is widely used in current factories, because it could inspect the in-process object easily, quickly, accurately. The following tasks were mainly investigated and implemented. We obtained more precise data by corning camera's distortion, compensating slit-beam error and revising acquired image. Much more, we made similar curves or surface with B-spline approximation for precision. Until now, there have been many case study of shape recognition. But it was uncompatible to apply to the field, because it had taken too many processing time and has frequent recognition failure. This paper propose recognition algorithm that prevent such errors and give applications to the field.

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Detection of Apple Defects Using Machine Vision (컴퓨터 시각에 의한 사과 결점 검출)

  • 서상룡;성제훈
    • Journal of Biosystems Engineering
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    • v.22 no.2
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    • pp.217-226
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    • 1997
  • This study was to develop a machine vision system to detect and to discriminate 5 kinds of apple surface defectbruise, decay. fleck, worm hole and scar. To detect the defects from an image of apple, thresholding technique was applied to images on various frames (R, G, B, H, S and I) of the color machine vision and an image of near infrared (NIR). To discriminate the detected region of defect, various features of the 5 kind defect regions were extracted from the 4 kinds of images selected above. The features were size of area, roundness, axes length ratio, mean and valiance of pixel values, standard deviation of real part of amplitude spectrum in frequency domain obtained by Fourier transform of pixel data and mean and standard deviation of power spectrum obtained by the same transform of pixel data. Routines to discriminate the defects from the features of image were developed and tested to prove their validity. The test resulted that I-frame and NIR images were the most desirable. Accuracies of the two images to discriminate the defects were noted as 76% and 77%, respectively.

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A study on vision seam tracking system at lap joints (겹치기이음에서 용접선 시각 추적 시스템에 관한 연구)

  • 신정식;김재웅;나석주;최칠룡
    • Journal of Welding and Joining
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    • v.9 no.2
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    • pp.20-28
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    • 1991
  • The main subject of this study is the construction of an automatic welding system that has the capability to trace the weld seam in GMA welding of lap joints. The system was composed of a vision sensor, moving torch, and personal computer(IBM-PC). In the developed vision sensor, an image was captured by the frame grabber at the time of short circuit during welding. The threshold method was adopted for determining the structured light and the central difference method for detecting the weld joint. And the seam tracing of the torch was performed by using the data regeneration algorithm. In this system using the image at the time of short circuit, weld seam tracking was performed without any relations to arc light and spatters.

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Intelligent User Pattern Recognition based on Vision, Audio and Activity for Abnormal Event Detections of Single Households

  • Jung, Ju-Ho;Ahn, Jun-Ho
    • Journal of the Korea Society of Computer and Information
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    • v.24 no.5
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    • pp.59-66
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    • 2019
  • According to the KT telecommunication statistics, people stayed inside their houses on an average of 11.9 hours a day. As well as, according to NSC statistics in the united states, people regardless of age are injured for a variety of reasons in their houses. For purposes of this research, we have investigated an abnormal event detection algorithm to classify infrequently occurring behaviors as accidents, health emergencies, etc. in their daily lives. We propose a fusion method that combines three classification algorithms with vision pattern, audio pattern, and activity pattern to detect unusual user events. The vision pattern algorithm identifies people and objects based on video data collected through home CCTV. The audio and activity pattern algorithms classify user audio and activity behaviors using the data collected from built-in sensors on their smartphones in their houses. We evaluated the proposed individual pattern algorithm and fusion method based on multiple scenarios.

Machine Vision Algorithm Design for Remote Control External Defect Inspection

  • Kang, Jin-Su;Kim, Young-Hyung;Yoon, Sang-Goo;Lee, Yong-Hwan
    • Journal of Platform Technology
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    • v.10 no.3
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    • pp.21-29
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    • 2022
  • Recently, the scope of the smart factory has been expanded, and process research to minimize the part that requires manpower in many processes is increasing. In the case of detecting defects in the appearance of small products, precise verification using a vision system is required. Reliability and speed of inspection are inefficient for human inspection. In this paper, we propose an algorithm for inspecting product appearance defects using a machine vision system. In the case of the remote control targeted in this paper, the appearance is different for each product. Due to the characteristics of the remote control product, the data obtained using two cameras is compared with the master data after denoising and stitching steps are completed. When the algorithm presented in this paper is used, it is possible to detect defects in a shorter time and more accurately compared to the existing human inspection.