146 lines
10 KiB
HTML
146 lines
10 KiB
HTML
<?php include('../inc/pageheader_eng.php'); ?>
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<link rel="stylesheet" href="../css/rnd.css">
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<script src="../js/picLoad.js"></script>
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<script src="../js/rnd.js?v=190507"></script>
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<?php include('../inc/menubar_eng.php'); ?>
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<section>
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<h1 class="hidden">Start of Content</h1>
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<article class="subBanner">
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<h2 class="hidden">서브배너</h2>
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<div class="inner">
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<div class="subBannerInfo">
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<ul>
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<li>REFERENCE</li>
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<li>Provides diverse solutions with Innovative technology for customer satisfaction</li>
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</ul>
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</div><!--end of subBannerInfo-->
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</div><!--end of inner-->
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</article><!--end of subBanner-->
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<div class="siteIndex">
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<div class="inner">
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REFERENCE 》 Deep Learning-based accident image detection system
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</div>
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</div><!--end of siteIndex-->
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<article class="rndContainer">
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<h2 class="hidden">REFERENCE</h2>
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<p><a href="./RnD_lunar.html">></a></p>
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<div class="rndWrapper">
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<div class="deepLearning">
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<h2 class="hidden">딥러닝 기반 사고영상 감지 시스템</h2>
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<ul class="clearfix">
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<li class="bigPic1">
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<iframe width="100%" height="100%" src="https://www.youtube.com/embed/8WVW2Di2Vu4" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
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</li>
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<li>
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<dl>
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<dt>Deep Learning-based accident image detection system</dt>
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<dd>
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Deep-learning based accident image detection system detects moving & target objects within
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CCTV surveillance range and automatically recognize and alert emergency situation such as
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accidents, reverse driving and fire specified in the management guideline. Compared to computer
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vision-based video analysis techniques, this AI based system is less influenced by weather or place and detects accidents with higher accuracy.
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</dd>
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<dd>
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<ul class="clearfix">
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<li><img src="../images/video_img_01.png" alt="첫 비디오"></li>
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<li><img src="../images/video_img_02.png" alt="두번째 비디오"></li>
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<li><img src="../images/video_img_04.png" alt="세번째 비디오"></li>
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<!--<li><video src="./avi/시퀀스_01_33.mp4" type="video/mp4" controls></li>-->
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</ul>
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</dd>
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</dl>
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</li>
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</ul>
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<div class="rndInfo">
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<div class="innerScroll">
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<ul class="techIntroduce">
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<li class="techIntroduceTitle">1) Technology Introduction : Deep Learning-based accident image detection system</li>
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<li>
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Object Detection is a research field that analyzes video information by computer instead of a
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person. Object detection technology is used variously in our daily life including pedestrian
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detection, vehicle and road sign recognition by analyzing CCTV video. Especially, in the field of
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traffic flow monitoring, the nationwide road network using CCTV is constantly monitored. In some
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major section, an accident recognition system is in operation to automatically detect and inform
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unexpected situations such as traffic jams, collision and falling objects and so forth. Typical
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accident detection systems are equipped with algorithms based on traditional vision technology,
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but it is highly sensitive to changes in surrounding environment such as luminance and weather,
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resulting in pre-detection and detection errors. To solve this problem, DBNtech developed an
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accident detection system that can detect objects in video like humans in various environments
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using Deep Learning-based object detection technology. In collaboration with KICT(KOREA
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INSTITUTE of CIVIL ENGINEERING and BUILDING TECHNOLOGY), we have completed an accident
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detection system optimized for various environments and have set demonstrate-installed and delivered the system on since 2019.
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</li>
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</ul><!--end of techIntroduce-->
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<ul class="techIntroduce">
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<li class="techIntroduceTitle">２) Related images</li>
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<li class="techIntroduceImg">
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<span><img src="../images/reference/deep/one.PNG" alt="레티나넷"></span>
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<span><img src="../images/reference/deep/two.PNG" alt="SSD and YOLO"></span>
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</li>
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</ul><!--end of techIntroduce-->
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<ul class="techIntroduce">
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<li class="techIntroduceTitle">3) Application of Deep Learning-based Object detection</li>
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<li class="techIntroduceHandle clearfix">
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<dl>
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<dt>A. Detection of unusual event (accident) through CCTV image analysis</dt>
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<dd><img src="../images/reference/deep/1.PNG" alt=""></dd>
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<dd>
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1. Detection of stop(or collision) and reverse driving<br />
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2. Pedestrian detection<br />
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3. Fire and smoke detection<br />
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</dd>
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</dl>
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<dl>
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<dt>B. Character detection in images</dt>
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<dd><img src="../images/reference/deep/2.PNG" alt=""></dd>
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<dd>
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1. Card number recognition<br />
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2. Vehicle’s license plate recognition<br />
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</dd>
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</dl>
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<dl>
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<dt>C. Object recognition in space</dt>
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<dd><img src="../images/reference/deep/3.PNG" alt=""></dd>
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<dd>
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1. Product recognition for unattended store payment system<br />
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2. Automatic inventory check-up in refrigerator<br />
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</dd>
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</dl>
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<dl>
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<dt>D. Behavioral pattern analysis</dt>
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<dd><img src="../images/reference/deep/4.PNG" alt=""></dd>
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<dd>
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1. Human motion tracking<br />
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2. Calculation of human’s action radius<br />
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</dd>
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</dl>
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</li>
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</ul><!--end of techIntroduce-->
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</div><!--end of innerScroll-->
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</div><!--end of rndInfo-->
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</div><!--end of deepLearning-->
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</div><!--end of rndWrapper-->
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</article><!--end of rndContainer-->
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<div class="fixMenu">
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<p class="btn_fix"></p>
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<ul>
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<li><a href="#" alt="딥러닝 기반 사고영상 감지 시스템">Deep Learning-based accident image detection system</a></li>
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<li><a href="#" alt="월면 크레이터 자동 인식 시스템">Automatic detection system of craters on the Lunar surface</a></li>
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<li><a href="#" alt="시추정보 기반의 액상화 모델링 및 3차원 분석 모듈">liquefaction modeling and 3D analysis module based on Drilling information </a></li>
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<li><a href="#" alt="도로표지관리/안내시스템">Road sign management/guide system</a></li>
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</ul>
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</div><!--end of fixMenu-->
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</section><!--end of section, mainPage-->
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<?php include('../inc/footer_eng.php'); ?>
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</body>
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</html> |