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Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
ÇØ»ó °´Ã¼ °ËÃâ °í¼Ó 󸮸¦ À§ÇÑ ¿µ»ó Àüó¸® ¾Ë°í¸®Áò ¼³°è¿Í µö·¯´× ±â¹ÝÀÇ ÅëÇÕ ½Ã½ºÅÛ |
¿µ¹®Á¦¸ñ(English Title) |
Design of Video Pre-processing Algorithm for High-speed Processing of Maritime Object Detection System and Deep Learning based Integrated System |
ÀúÀÚ(Author) |
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Hyun-hak Song
Hyo-chan Lee
Sung-ju Lee
Ho-seok Jeon
Tae-ho Im
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¿ø¹®¼ö·Ïó(Citation) |
VOL 21 NO. 04 PP. 0117 ~ 0126 (2020. 08) |
Çѱ۳»¿ë (Korean Abstract) |
ÇØ»ó °´Ã¼ ÀνÄÀº ÀÚÀ²¿îÇ×¼±¹Ú(MASS)ÀÇ Áö´ÉÇü º¸Á¶ ½Ã½ºÅÛÀ¸·Î½á, ¼±ÀåÀÌ À°¾ÈÀ¸·Î ÇØ»ó ÁÖº¯ÀÇ Ãæµ¹ À§Ç輺ÀÌ ÀÖ´Â ºÎÀ¯¹°À» È®ÀÎÇÏ´ø Á¤º¸¸¦ ÄÄÇ»Å͸¦ ÅëÇØ ÀÚµ¿À¸·Î ÀνÄÇÏ¿© »ç¶÷ÀÌ È®ÀÎÇÏ´Â ¹æ¹ý°ú À¯»çÇÑ Á¤È®µµ·Î ÀÎÁöÇÏ´Â ¹æ¹ýÀ» ¸»ÇÑ´Ù. ¼±¹Ú ÁÖº¯ÀÇ ¹°Ã¼¸¦ ÀνÄÇÏ´Â ¹æ¹ýÀ¸·Î ±âÁ¸¿¡´Â ·¹ÀÌ´õ³ª ¼Ò³ª¿Í °°Àº ÀåÄ¡·ÎºÎÅÍ ¼öÁýµÈ Á¤º¸¸¦ ÅëÇØ È®ÀÎÇÏ¿´Áö¸¸, ÀΰøÁö´ÉÀÇ ±â¼úÀÌ ¹ß´ÞÇÏ¸é¼ ¼±¹Ú Áö´ÉÇü CCTV¸¦ ÅëÇØ ¿îÇ× Ç׷ο¡ ÀÖ´Â ´Ù¾çÇÑ ºÎÀ¯¹°À» ÀνÄÇÏ´Â °ÍÀÌ °¡´ÉÇÏ´Ù. ÇÏÁö¸¸, ÀÚÀ² ¼±¹ÚÀÇ ´Ù¾çÇÑ ¿ä±¸»çÇ×°ú º¹À⼺ ¶§¹®¿¡ ¿µ»ó µ¥ÀÌÅÍÀÇ Ã³¸®¼Óµµ°¡ ´À·ÁÁö°Ô µÈ´Ù¸é ¿øÈ°ÇÑ ¼ºñ½º Áö¿øÀº ¹°·Ð ¾ÈÀü¼ºµµ º¸ÀåÇÒ ¼ö ¾ø°Ô µÈ´Ù. ÀÌ·¯ÇÑ ¹®Á¦¸¦ ÇØ°áÇÏ°íÀÚ º» ³í¹®¿¡¼´Â ÇØ»ó °´Ã¼¸¦ °ËÃâÇÏ´Â µ¥ ÀÖ¾î ¿µ»ó µ¥ÀÌÅÍÀÇ ¿¬»ê·®À» ÃÖ¼ÒÈÇÏ¿© 󸮼ӵµ¸¦ ³ôÀ̱â À§ÇÑ ¿¬±¸¸¦ ÁøÇàÇÏ¿´´Ù. ÇØ»ó °´Ã¼ ÀνÄÀÇ °ü½É ¿µ¿ªÀ» È®º¸Çϱâ À§Çؼ´Â ÀϹÝÀûÀ¸·Î ¼öÆò¼±À» ã´Âµ¥ ±âÁ¸ ¿¬±¸µéÀº ÇãÇÁ º¯È¯ ¾Ë°í¸®ÁòÀ» È°¿ëÇÏÁö¸¸ º» ³í¹®¿¡¼´Â ¼Óµµ¸¦ °³¼±Çϱâ À§ÇØ ÀÌÁøÈ ¾Ë°í¸®ÁòÀ» ÃÖÀûÈÇÏ¿© ½ÇÁ¦ °´Ã¼ÀÇ À§Ä¡¿Í À¯»çÇÑ ¿µ¿ªÀ» ã´Â »õ·Î¿î ¹æ¹ýÀ» Á¦¾ÈÇÑ´Ù. ¶ÇÇÑ, Á¦¾ÈÇÏ´Â ¹æ¹ýÀÇ À¯¿ë¼ºÀ» Áõ¸íÇϱâ À§ÇØ µö·¯´× CNNÀ» È°¿ëÇÏ¿© ÇØ»ó °´Ã¼ ÀÎ½Ä ½Ã½ºÅÛÀ» ±¸ÇöÇÔÀ¸·Î½á ¾Ë°í¸®ÁòÀÇ ¼º´ÉÀ» Æò°¡ÇÏ¿´´Ù. Á¦¾ÈÇÏ´Â ¾Ë°í¸®ÁòÀº ±âÁ¸ ¹æ¹ýÀÇ ÀÎ½Ä Á¤È®µµ¸¦ À¯ÁöÇÏ¸é¼ ¾à 4¹è ÀÌ»óÀÇ ºü¸¥ ¼º´ÉÀ» ¾òÀ» ¼ö ÀÖ¾ú´Ù.
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¿µ¹®³»¿ë (English Abstract) |
A maritime object detection system is an intelligent assistance system to maritime autonomous surface ship(MASS). It detects automatically floating debris, which has a clash risk with objects in the surrounding water and used to be checked by a captain with a naked eye, at a similar level of accuracy to the human check method. It is used to detect objects around a ship. In the past, they were detected with information gathered from radars or sonar devices. With the development of artificial intelligence technology, intelligent CCTV installed in a ship are used to detect various types of floating debris on the course of sailing. If the speed of processing video data slows down due to the various requirements and complexity of MASS, however, there is no guarantee for safety as well as smooth service support. Trying to solve this issue, this study conducted research on the minimization of computation volumes for video data and the increased speed of data processing to detect maritime objects. Unlike previous studies that used the Hough transform algorithm to find the horizon and secure the areas of interest for the concerned objects, the present study proposed a new method of optimizing a binarization algorithm and finding areas whose locations were similar to actual objects in order to improve the speed. A maritime object detection system was materialized based on deep learning CNN to demonstrate the usefulness of the proposed method and assess the performance of the algorithm. The proposed algorithm performed at a speed that was 4 times faster than the old method while keeping the detection accuracy of the old method.
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Å°¿öµå(Keyword) |
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Ship Detection
Deep Learning
Image Processing
Binarization
Horizon Detection
Multi Connected-component Labeling
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