ÇѱÛÁ¦¸ñ(Korean Title) |
Á÷¹° °áÇÔ¿µ¿ªÀ» Ç¥½ÃÇÑ ¿µ»ó¿¡ ´ëÇÑ ½ÇÇèÀû °íÂû |
¿µ¹®Á¦¸ñ(English Title) |
Experimental Remarks on Manually Attentive Fabric Defect Regions |
ÀúÀÚ(Author) |
Rakhmatov Shohruh
ÃÖÇö¿µ
°íÀçÇÊ
Hyeon-yeong Choi
Jaepil Ko
|
¿ø¹®¼ö·Ïó(Citation) |
VOL 23 NO. 01 PP. 0442 ~ 0444 (2019. 05) |
Çѱ۳»¿ë (Korean Abstract) |
Á÷¹°°áÇÔ ºÐ·ù´Â ¿ø´Ü Ç°Áú°ü¸®¿¡ ÀÖ¾î Áß¿äÇÑ ¹®Á¦ÀÌ´Ù. ÇÏÁö¸¸, ´Ù¾çÇÑ °áÇÔÀÇ Á¾·ù¸¦ ¿µ»óÀ¸·Î ½Äº°Çϱ⠾î·Æ±â ¶§¹®¿¡ ÀÚµ¿È°¡ ¾î·Æ´Ù. µû¶ó¼ Á÷¹°°áÇÔ ºÐ·ù´Â ´ëºÎºÐ »ç¶÷¿¡°Ô ÀÇÁ¸ÇÏ°í ÀÖ´Ù. º» ³í¹®¿¡¼´Â, À̸¦ ÇØ°áÇϱâ À§ÇØ Á÷¹°°áÇÔ ºÐ·ù ¹®Á¦¿¡ CNNÀ» Àû¿ëÇÑ´Ù. ¶ÇÇÑ CNNÀÇ ÇнÀÀ» º¸´Ù ½±°Ô Çϱâ À§ÇÏ¿©, »ç¶÷ÀÌ ¿µ»ó¿¡ °áÇÔ ¿µ¿ªÀ» Ç¥½ÃÇÏ´Â ¹æ¹ýÀ» Á¦¾ÈÇÑ´Ù. º» ³í¹®¿¡¼´Â Á¦¾È¹æ¹ý°ú ¿øº»¿µ»ó¿¡ ´ëÇÑ ºñ±³½ÇÇèÀ» ¼öÇàÇÏ¿©, Á¦¾È¹æ¹ýÀÌ ÇнÀ¿¡ È¿°ú°¡ ÀÖ´Ù´Â °ÍÀ» È®ÀÎÇÏ¿´´Ù. |
¿µ¹®³»¿ë (English Abstract) |
Fabric defect classification is an important issue in fabric quality control. However, automated classification is difficult because it is hard to identify various types of defects in images. classification of fabric defects mostly rely on human ability. In this paper, to solve this problem we apply Convolutional Neural Networks (CNN) for fabric defect classification. To make training CNN easier, we propose a method that is manually attentive defect regions in images. we compare the proposed method with the original image and confirm that the proposed method is effective for learning. |
Å°¿öµå(Keyword) |
Deep Learning
Febric Defect Classification
Convolutional Neural Network
Manual Attention
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