¼¼°èÀÇ ÇÕ¼º°ö ½Å°æ¸Á ½ÃÀå
Convolutional Neural Networks
»óǰÄÚµå : 1745031
¸®¼­Ä¡»ç : Global Industry Analysts, Inc.
¹ßÇàÀÏ : 2025³â 06¿ù
ÆäÀÌÁö Á¤º¸ : ¿µ¹® 220 Pages
 ¶óÀ̼±½º & °¡°Ý (ºÎ°¡¼¼ º°µµ)
US $ 5,850 £Ü 8,496,000
PDF (Single User License) help
PDF º¸°í¼­¸¦ 1¸í¸¸ ÀÌ¿ëÇÒ ¼ö ÀÖ´Â ¶óÀ̼±½ºÀÔ´Ï´Ù. Àμâ´Â °¡´ÉÇϸç Àμ⹰ÀÇ ÀÌ¿ë ¹üÀ§´Â PDF ÀÌ¿ë ¹üÀ§¿Í µ¿ÀÏÇÕ´Ï´Ù.
US $ 17,550 £Ü 25,489,000
PDF (Global License to Company and its Fully-owned Subsidiaries) help
PDF º¸°í¼­¸¦ µ¿ÀÏ ±â¾÷ÀÇ ¸ðµç ºÐÀÌ ÀÌ¿ëÇÒ ¼ö ÀÖ´Â ¶óÀ̼±½ºÀÔ´Ï´Ù. Àμâ´Â °¡´ÉÇϸç Àμ⹰ÀÇ ÀÌ¿ë ¹üÀ§´Â PDF ÀÌ¿ë ¹üÀ§¿Í µ¿ÀÏÇÕ´Ï´Ù.


Çѱ۸ñÂ÷

¼¼°èÀÇ ÇÕ¼º°ö ½Å°æ¸Á ½ÃÀåÀº 2030³â±îÁö 1,160¾ï ´Þ·¯¿¡ À̸¦ Àü¸Á

2024³â¿¡ 144¾ï ´Þ·¯·Î ÃßÁ¤µÇ´Â ÇÕ¼º°ö ½Å°æ¸Á ¼¼°è ½ÃÀåÀº 2024-2030³â°£ CAGR 41.6%·Î ¼ºÀåÇÏ¿© 2030³â¿¡´Â 1,160¾ï ´Þ·¯¿¡ À̸¦ °ÍÀ¸·Î ¿¹ÃøµË´Ï´Ù. º» º¸°í¼­¿¡¼­ ºÐ¼®ÇÑ ºÎ¹® Áß ÇϳªÀÎ Çϵå¿þ¾î ÄÄÆ÷³ÍÆ®´Â CAGR 45.9%¸¦ ³ªÅ¸³»°í, ºÐ¼® ±â°£ Á¾·á½Ã¿¡´Â 787¾ï ´Þ·¯¿¡ À̸¦ °ÍÀ¸·Î ¿¹ÃøµË´Ï´Ù. ¼ÒÇÁÆ®¿þ¾î ÄÄÆ÷³ÍÆ® ºÐ¾ßÀÇ ¼ºÀå·üÀº ºÐ¼® ±â°£¿¡ CAGR 33.8%·Î ÃßÁ¤µË´Ï´Ù.

¹Ì±¹ ½ÃÀåÀº 38¾ï ´Þ·¯, Áß±¹Àº CAGR 39.2%¸¦ º¸ÀÏ °ÍÀ¸·Î ¿¹Ãø

¹Ì±¹ÀÇ ÇÕ¼º°ö ½Å°æ¸Á ½ÃÀåÀº 2024³â¿¡ 38¾ï ´Þ·¯·Î ÃßÁ¤µË´Ï´Ù. ¼¼°è 2À§ °æÁ¦´ë±¹ÀÎ Áß±¹Àº 2030³â±îÁö 170¾ï ´Þ·¯ ±Ô¸ð¿¡ À̸¦ °ÍÀ¸·Î ¿¹ÃøµÇ¸ç, ºÐ¼® ±â°£ÀÎ 2024-2030³â CAGRÀº 39.2%·Î ÃßÁ¤µË´Ï´Ù. ±âŸ ÁÖ¸ñÇØ¾ß ÇÒ Áö¿ªº° ½ÃÀåÀ¸·Î¼­´Â ÀϺ»°ú ij³ª´Ù°¡ ÀÖÀ¸¸ç, ºÐ¼® ±â°£Áß CAGRÀº °¢°¢ 38.6%¿Í 35.6%¸¦ º¸ÀÏ °ÍÀ¸·Î ¿¹ÃøµË´Ï´Ù. À¯·´¿¡¼­´Â µ¶ÀÏÀÌ CAGR 28.5%¸¦ º¸ÀÏ Àü¸ÁÀÔ´Ï´Ù.

¼¼°èÀÇ ÇÕ¼º°ö ½Å°æ¸Á ½ÃÀå - ÁÖ¿ä µ¿Çâ°ú ÃËÁø¿äÀÎ Á¤¸®

¿Ö ÇÕ¼º°ö ½Å°æ¸ÁÀº AI¸¦ Ȱ¿ëÇÑ ½Ã°¢°ú ÀνÄÀÇ °æ°è¸¦ º¯È­½Ã۴°¡?

ÇÕ¼º°ö ½Å°æ¸Á(CNN)Àº ÀΰøÁö´É, ƯÈ÷ À̹ÌÁö Àνİú ÆÐÅÏ ÀÎ½Ä ºÐ¾ß¸¦ ¹ßÀü½ÃŰ´Â ±âÃʰ¡ µÇ¾ú½À´Ï´Ù. Àΰ£ÀÇ ½Ã°¢ ¿µ¿ª¿¡¼­ ¿µ°¨À» ¾òÀº CNNÀº ÀÔ·Â µ¥ÀÌÅͷκÎÅÍ Æ¯Â¡ÀÇ °ø°£Àû °èÃþÀ» ÀÚµ¿À¸·Î ÀûÀÀÀûÀ¸·Î ÇнÀÇϵµ·Ï ¼³°èµÇ¾î ½Ã°¢Àû À̹ÌÁö ºÐ¼®¿¡ ¸Å¿ì È¿°úÀûÀÔ´Ï´Ù. ÄÁº¼·ç¼Ç °èÃþ, Ç®¸µ °èÃþ, ¿ÏÀü ¿¬°á °èÃþÀ¸·Î ±¸¼ºµÈ CNNÀÇ °èÃþÀû ¾ÆÅ°ÅØÃ³´Â °¡ÀåÀÚ¸®¿Í ¸ð¼­¸®¿Í °°Àº ³·Àº ¼öÁØÀÇ ÆÐÅÏÀ» °¨ÁöÇÏ°í ¸ð¾ç, ¾ó±¼, ¹°Ã¼ µî º¸´Ù º¹ÀâÇÑ ±¸Á¶¸¦ Á¡ÁøÀûÀ¸·Î ½Äº°ÇÒ ¼ö ÀÖ°Ô ÇØÁÝ´Ï´Ù. CNNÀÇ °­Á¡Àº ¼öÀÛ¾÷¿¡ ÀÇÇÑ Æ¯Â¡ ¼³°è ¾øÀÌ ´Ù¾çÇÑ ±Ô¸ð¿Í ¹æÇâ¿¡ °ÉÃÄ Æ¯Â¡À» ÀϹÝÈ­ÇÒ ¼ö ÀÖ´Â ´É·Â¿¡ ÀÖ½À´Ï´Ù. µû¶ó¼­ Á¤È®µµ°¡ ³ôÀ» »Ó¸¸ ¾Æ´Ï¶ó ´Ù¾çÇÑ µ¥ÀÌÅÍ ¼¼Æ®¿¡ ´ëÇÑ È®À强ÀÌ ÀÖ½À´Ï´Ù. ½º¸¶Æ®Æù, ¸ð´ÏÅ͸µ ½Ã½ºÅÛ, »ê¾÷¿ë ¼¾¼­¸¦ ÅëÇÑ ½Ã°¢ µ¥ÀÌÅͰ¡ Æø¹ßÀûÀ¸·Î Áõ°¡ÇÔ¿¡ µû¶ó °í¼º´É CNN ¸ðµ¨¿¡ ´ëÇÑ ¼ö¿ä°¡ ºü¸£°Ô Áõ°¡Çϰí ÀÖ½À´Ï´Ù. ´õ ¸¹Àº »ê¾÷¿¡¼­ ½Ç½Ã°£ ½Ã°¢Àû ÇØ¼®ÀÇ °¡Ä¡¸¦ ÀνÄÇÔ¿¡ µû¶ó CNNÀº Çй®Àû °³³ä¿¡¼­ ÀÚµ¿È­, Áö´ÉÈ­, ¿î¿µ Á¤È®µµ¸¦ ÃËÁøÇÏ´Â ÇʼöÀûÀÎ µµ±¸·Î ÁøÈ­Çϰí ÀÖ½À´Ï´Ù.

»ê¾÷º° ¿ëµµ´Â °¢ ºÐ¾ß¿¡¼­ CNNÀÇ Ã¤ÅÃÀ» ¾î¶»°Ô ÃËÁøÇϰí Àִ°¡?

ÇÕ¼º°ö ½Å°æ¸ÁÀº º¹ÀâÇÑ À̹ÌÁö ¹× ºñµð¿À µ¥ÀÌÅÍ¿¡¼­ ÀÇ¹Ì ÀÖ´Â ÅëÂû·ÂÀ» µµÃâÇÏ´Â °íÀ¯ÇÑ ´É·ÂÀ¸·Î ÀÎÇØ ´Ù¾çÇÑ »ê¾÷ ºÐ¾ß¿¡¼­ äÅÃÀÌ °¡¼ÓÈ­µÇ°í ÀÖ½À´Ï´Ù. ÇコÄÉ¾î ºÐ¾ß¿¡¼­´Â CNNÀÌ ¿¢½º·¹ÀÌ, MRI, º´¸®Á¶Á÷ÇÐ ¿µ»óÀ» ºÐ¼®ÇÏ´Â Áø´Ü ½Ã½ºÅÛÀ» Áö¿øÇÏ¿© ¾Ï, °áÇÙ, ½Å°æÁúȯ µîÀÇ Áúº´À» Àü¹®°¡ ¼öÁØÀÇ Á¤È®µµ·Î °¨ÁöÇϰí ÀÖ½À´Ï´Ù. ÀÚµ¿Â÷ ¾÷°è¿¡¼­´Â CNNÀÌ ÀÚÀ²ÁÖÇàÂ÷ ºñÀü ½Ã½ºÅÛÀÇ ÇÙ½É ¿ªÇÒÀ» ¼öÇàÇϸç Â÷¼± °¨Áö, ¹°Ã¼ ÀνÄ, º¸ÇàÀÚ ÃßÀû, ½Ç½Ã°£ ÀÇ»ç°áÁ¤À» °¡´ÉÇÏ°Ô ÇÕ´Ï´Ù. ¼Ò¸Å¾÷¿¡¼­´Â Àç°í ¸ð´ÏÅ͸µ, ¼îÇΰ´ Çൿ ºÐ¼®, ½Ã°¢Àû °Ë»ö¿¡ CNNÀÌ È°¿ëµÇ°í ÀÖÀ¸¸ç, ³ó¾÷¿¡¼­´Â ½Ä¹° Áúº´ °¨Áö, ÀÛ¹° °Ç°­ ¸ð´ÏÅ͸µ, ½Ã°¢ ±â¹Ý ¸ðµ¨À» ÅëÇÑ ÀÚµ¿ ¼öÈ®¿¡ Ȱ¿ëµÇ°í ÀÖ½À´Ï´Ù. º¸¾È ¹× ±¹¹æ ºÐ¾ß¿¡¼­´Â ¾ó±¼ ÀνÄ, °¨½Ã ¿µ»ó ºÐ¼®, À§Çù °¨Áö ½Ã½ºÅÛ¿¡ CNNÀÌ È°¿ëµÇ°í ÀÖÀ¸¸ç, ÀüÀÚ»ó°Å·¡ Ç÷§Æû¿¡¼­´Â »óǰ ÅÂ±× ÁöÁ¤, À̹ÌÁö ºÐ·ù, Áõ°­Çö½Ç(AR) ¼îÇÎ °æÇè¿¡ CNNÀÌ È°¿ëµÇ°í ÀÖ½À´Ï´Ù. ÆÐ¼ÇÀ̳ª ¹Ìµð¾î¿Í °°Àº Å©¸®¿¡ÀÌÆ¼ºê ºÐ¾ß¿¡¼­µµ CNNÀº µðÀÚÀÎ Ãßõ°ú À̹ÌÁö ±â¹Ý ÄÁÅÙÃ÷ Å¥·¹À̼ÇÀÇ Çõ½ÅÀ» ÁÖµµÇϰí ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ ´Ù¾çÇÏ°í ¿µÇâ·Â ÀÖ´Â ¿ëµµ´Â CNNÀÇ È°µ¿ ¿µ¿ªÀ» ³ÐÈú »Ó¸¸ ¾Æ´Ï¶ó, AI°¡ Çö½Ç ¼¼°èÀÇ Æ¯Á¤ ºÐ¾ß¿¡ ƯȭµÈ ¸Æ¶ô¿¡¼­ ´Þ¼ºÇÒ ¼ö ÀÖ´Â °ÍÀÇ ÇѰ踦 ³ÐÈ÷°í ÀÖ½À´Ï´Ù.

CNNÀÇ ¼º´É°ú Á¢±Ù¼ºÀ» È®ÀåÇÏ´Â ±â¼ú Çõ½ÅÀº ¾î¶² °ÍÀÌ ÀÖÀ»±î?

ÄÄÇ»ÆÃ Çϵå¿þ¾î, ¾Ë°í¸®Áò ÃÖÀûÈ­, AI ÇÁ·¹ÀÓ¿öÅ©ÀÇ ¹ßÀüÀº ÇÕ¼º°ö ½Å°æ¸ÁÀÇ ¼º´É°ú Á¢±Ù¼ºÀ» Å©°Ô Çâ»ó½ÃÄ×À¸¸ç, ResNet, Inception, EfficientNet, MobileNet°ú °°Àº º¸´Ù È¿À²ÀûÀÎ ³×Æ®¿öÅ© ¾ÆÅ°ÅØÃ³ÀÇ °³¹ß·Î ¼ö·Å ¼Óµµ Çâ»ó, ¸Þ¸ð¸® »ç¿ë·® °¨¼Ò, Àüü ÀÛ¾÷ÀÇ Á¤È®µµ Çâ»óÀ» ½ÇÇöÇϰí ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ ¸ðµ¨Àº ±â¿ï±â ¼Ò½Ç ¹× ¿À¹öÇÇÆÃ°ú °°Àº ¹®Á¦¸¦ ÇØ°áÇÏ¿© CNNÀ» ´õ¿í °ß°íÇϰí ÀϹÝÈ­ÇÒ ¼ö ÀÖµµ·Ï ÇÕ´Ï´Ù. ÀüÀÌ ÇнÀÀ» Ȱ¿ëÇϸé ImageNet°ú °°Àº ´ë±Ô¸ð µ¥ÀÌÅÍ ¼¼Æ®·Î CNNÀ» »çÀü ÈÆ·ÃÇϰí ÃÖ¼ÒÇÑÀÇ µ¥ÀÌÅͷΠƯÁ¤ ÀÛ¾÷¿¡ ¸Â°Ô ¹Ì¼¼ Á¶Á¤ÇÒ ¼ö Àֱ⠶§¹®¿¡ ¼Ò±Ô¸ð Á¶Á÷ÀÇ ¸®¼Ò½º ºÎ´ãÀ» ÁÙÀÏ ¼ö ÀÖ½À´Ï´Ù. Çϵå¿þ¾î Ãø¸é¿¡¼­´Â GPU, TPU, ¿§Áö AI ĨÀÇ µîÀåÀ¸·Î ¸®¼Ò½º¿¡ Á¦¾àÀÌ Àִ ȯ°æ¿¡¼­µµ ´ë±Ô¸ð·Î CNNÀ» ÈÆ·ÃÇÏ°í ¹èÆ÷ÇÒ ¼ö ÀÖ°Ô µÇ¾ú½À´Ï´Ù. ¿¡Áö ¹èÄ¡´Â ½Ç½Ã°£ ¸ð´ÏÅ͸µÀ̳ª Â÷·®¿ë ¿µ»ó ó¸®¿Í °°ÀÌ ³·Àº Áö¿¬À» ÇÊ¿ä·Î ÇÏ´Â ¿ëµµ¿¡¼­ ƯÈ÷ À¯¿ëÇÕ´Ï´Ù. ÇÑÆí, TensorFlow, PyTorch, Keras¿Í °°Àº ¿ÀǼҽº ÇÁ·¹ÀÓ¿öÅ©´Â CNN °³¹ß¿¡ ´ëÇÑ Á¢±ÙÀ» ¹ÎÁÖÈ­ÇÏ¿© °³¹ßÀÚ, ¿¬±¸ÀÚ, ±â¾÷ÀÌ ºü¸£°Ô ±¸ÃàÇÏ°í ¹Ýº¹ÇÒ ¼ö ÀÖµµ·Ï µ½°í ÀÖ½À´Ï´Ù. Ŭ¶ó¿ìµå Ç÷§Æû ¹× AI-as-a-service ¸ðµ¨°úÀÇ ÅëÇÕÀ¸·Î ÁøÀÔÀ庮Àº ´õ¿í ³·¾ÆÁö°í ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ ±â¼ú Çõ½ÅÀ¸·Î CNNÀº ´õ¿í È¿À²ÀûÀÌ°í ºñ¿ë È¿À²ÀûÀ̸ç, ´Ù¾çÇÑ »ê¾÷°ú ÀÌ¿ë »ç·ÊÀÇ ÆøÀ» ³ÐÇô°¡°í ÀÖ½À´Ï´Ù.

ÇÕ¼º°ö ½Å°æ¸ÁÀÇ ¼¼°è ¼ºÀåÀ» °¡¼ÓÇÏ´Â ÁÖ¿ä ¿äÀÎÀº?

ÇÕ¼º°ö ½Å°æ¸Á ½ÃÀåÀÇ ¼ºÀåÀº µ¥ÀÌÅÍ °¡¿ë¼º, ÀÚµ¿È­¿¡ ´ëÇÑ ¾÷°è ¼ö¿ä, AI ÀÎÇÁ¶óÀÇ Áö¼ÓÀûÀÎ °³¼±ÀÌ °áÇÕÇÏ¿© ÀÌ·ç¾îÁö°í ÀÖ½À´Ï´Ù. ÁÖ¿ä ¿äÀÎ Áß Çϳª´Â ½º¸¶Æ®Æù, IoT ±â±â, ¸ð´ÏÅ͸µ ½Ã½ºÅÛ, ¼Ò¼È ¹Ìµð¾î Ç÷§ÆûÀ» ÅëÇØ »ý¼ºµÇ´Â À̹ÌÁö ¹× µ¿¿µ»ó µ¥ÀÌÅÍÀÇ ±Þ°ÝÇÑ Áõ°¡ÀÔ´Ï´Ù. ±â¾÷µéÀº ½Ã°¢Àû °Ë»ç, ÀÇ»ç°áÁ¤, °í°´°úÀÇ »óÈ£ÀÛ¿ëÀ» ÀÚµ¿È­ÇÒ ¼ö ÀÖ´Â ¹æ¹ýÀ» Á¡Á¡ ´õ ¸¹ÀÌ Ã£°í ÀÖ½À´Ï´Ù. ƯÈ÷ ÇコÄɾî, ÀÚµ¿Â÷, Á¦Á¶, ¼Ò¸Å¾÷ µîÀÇ ºÐ¾ß¿¡¼­ µðÁöÅÐ ÀüȯÀÇ ÃßÁøÀ¸·Î AI ±â¹Ý ºñÀü ±â¼ú¿¡ ´ëÇÑ ÅõÀÚ°¡ °¡¼ÓÈ­µÇ°í ÀÖ½À´Ï´Ù. ÇÑÆí, ½Ç½Ã°£ ºÐ¼®°ú ¿§Áö ÄÄÇ»ÆÃÀÌ °­Á¶µÇ¸é¼­ ÀÓº£µðµå ±â±â¿¡¼­ È¿À²ÀûÀ¸·Î ÀÛµ¿ÇÏ´Â °æ·®È­µÈ CNN ¸ðµ¨ÀÇ Ã¤ÅÃÀÌ Áõ°¡Çϰí ÀÖ½À´Ï´Ù. Á¤ºÎ ¹× ±¹¹æ ºÐ¾ß¿¡¼­µµ AI¸¦ Ȱ¿ëÇÑ °¨½Ã ¹× º¸¾È ¼Ö·ç¼Ç¿¡ ÅõÀÚÇϰí ÀÖÀ¸¸ç, ÀÌ´Â ¼ö¿ä¸¦ ´õ¿í Áõ°¡½Ã۰í ÀÖ½À´Ï´Ù. ¶ÇÇÑ, Áõ°­Çö½Ç(AR), ·Îº¿°øÇÐ, ÀÚ¿¬¾î ó¸®¿Í °°Àº º¸¿ÏÀûÀÎ ±â¼ú°ú CNNÀÇ ÅëÇÕÀ¸·Î CNN ±â¹Ý ½Ã½ºÅÛÀÇ ¹üÀ§¿Í ¸Å·ÂÀÌ È®´ëµÇ°í ÀÖÀ¸¸ç, AI ±ÔÁ¦°¡ ¼º¼÷Çϰí Ã¥ÀÓ°¨ ÀÖ´Â AI °üÇàÀÌ ÁÖ·ù·Î ÀÚ¸® ÀâÀ¸¸é¼­ CNNÀº Åõ¸í¼º, °øÁ¤¼º, ¼³¸í°¡´É¼ºÀ» Çâ»ó½Ã۸ç ÁøÈ­Çϰí ÀÖ½À´Ï´Ù. ¹Ì·¡ÀÇ ¿ëµµ¿¡¼­ °ü·Ã¼º°ú ½Å·Ú¼ºÀ» È®º¸ÇÒ ¼ö ÀÖÀ» °ÍÀ¸·Î º¸ÀÔ´Ï´Ù. ÀÌ·¯ÇÑ ÈûµéÀÌ °áÇյǾî ÇÕ¼º°ö ½Å°æ¸ÁÀº Àü ¼¼°èÀûÀ¸·Î °­·ÂÇϰí Áö¼Ó °¡´ÉÇÑ ¼ºÀå ±Ëµµ¸¦ ±×¸®°í ÀÖ½À´Ï´Ù.

ºÎ¹®

ÄÄÆ÷³ÍÆ®(Çϵå¿þ¾î, ¼ÒÇÁÆ®¿þ¾î, ¼­ºñ½º), Àü°³(On-Premise, Ŭ¶ó¿ìµå), ¿ëµµ(À̹ÌÁö ¹× ºñµð¿À ÀνÄ, ÀÚ¿¬¾ð¾îó¸®, ÀÇ·á ¿µ»ó ºÐ¼®, ÀÚÀ²ÁÖÇàÂ÷, ·Îº¿¡¤Á¦Á¶, ±âŸ ¿ëµµ), ¾÷°èº°(ÇコÄɾî, ÀÚµ¿Â÷, ¼Ò¸Å ¹× E-Commerce, IT ¹× Åë½Å, Á¦Á¶, Ç×°ø¿ìÁÖ ¹× ¹æÀ§, ¿¡³ÊÁö ¹× À¯Æ¿¸®Æ¼, ±âŸ ¾÷°è)

Á¶»ç ´ë»ó ±â¾÷ ¿¹(ÃÑ 42°³»ç)

°ü¼¼ ¿µÇâ °è¼ö

Global Industry Analysts´Â º»»çÀÇ ±¹°¡, Á¦Á¶°ÅÁ¡, ¼öÃâÀÔ(¿ÏÁ¦Ç° ¹× OEM)À» ±â¹ÝÀ¸·Î ±â¾÷ÀÇ °æÀï·Â º¯È­¸¦ ¿¹ÃøÇß½À´Ï´Ù. ÀÌ·¯ÇÑ º¹ÀâÇÏ°í ´Ù¸éÀûÀÎ ½ÃÀå ¿ªÇÐÀº ÀÎÀ§ÀûÀÎ ¼öÀÍ¿ø°¡ Áõ°¡, ¼öÀͼº °¨¼Ò, °ø±Þ¸Á ÀçÆí µî ¹Ì½ÃÀû ¹× °Å½ÃÀû ½ÃÀå ¿ªÇÐ Áß¿¡¼­µµ ƯÈ÷ °æÀï»çµé¿¡°Ô ¿µÇâÀ» ¹ÌÄ¥ °ÍÀ¸·Î ¿¹ÃøµË´Ï´Ù.

Global Industry Analysts´Â ¼¼°è ÁÖ¿ä ¼ö¼® ÀÌÄÚ³ë¹Ì½ºÆ®(1,4,949¸í), ½ÌÅ©ÅÊÅ©(62°³ ±â°ü), ¹«¿ª ¹× »ê¾÷ ´Üü(171°³ ±â°ü)ÀÇ Àü¹®°¡µéÀÇ ÀǰßÀ» ¸é¹ÐÈ÷ °ËÅäÇÏ¿© »ýŰ迡 ¹ÌÄ¡´Â ¿µÇâÀ» Æò°¡ÇÏ°í »õ·Î¿î ½ÃÀå Çö½Ç¿¡ ´ëÀÀÇϰí ÀÖ½À´Ï´Ù. ¸ðµç ÁÖ¿ä ±¹°¡ÀÇ Àü¹®°¡¿Í °æÁ¦ÇÐÀÚµéÀÌ °ü¼¼¿Í ±×°ÍÀÌ ÀÚ±¹¿¡ ¹ÌÄ¡´Â ¿µÇâ¿¡ ´ëÇÑ ÀǰßÀ» ÃßÀû Á¶»çÇß½À´Ï´Ù.

Global Industry Analysts´Â ÀÌ·¯ÇÑ È¥¶õÀÌ ÇâÈÄ 2-3°³¿ù ³»¿¡ ¸¶¹«¸®µÇ°í »õ·Î¿î ¼¼°è Áú¼­°¡ º¸´Ù ¸íÈ®ÇÏ°Ô È®¸³µÉ °ÍÀ¸·Î ¿¹»óÇϰí ÀÖÀ¸¸ç, Global Industry Analysts´Â ÀÌ·¯ÇÑ »óȲÀ» ½Ç½Ã°£À¸·Î ÃßÀûÇϰí ÀÖ½À´Ï´Ù.

2025³â 4¿ù: Çù»ó ´Ü°è

À̹ø 4¿ù º¸°í¼­¿¡¼­´Â °ü¼¼°¡ ¼¼°è ½ÃÀå Àüü¿¡ ¹ÌÄ¡´Â ¿µÇâ°ú Áö¿ªº° ½ÃÀå Á¶Á¤¿¡ ´ëÇØ ¼Ò°³ÇÕ´Ï´Ù. ´ç»çÀÇ ¿¹ÃøÀº °ú°Å µ¥ÀÌÅÍ¿Í ÁøÈ­ÇÏ´Â ½ÃÀå ¿µÇâ¿äÀÎÀ» ±â¹ÝÀ¸·Î ÇÕ´Ï´Ù.

2025³â 7¿ù: ÃÖÁ¾ °ü¼¼ Àç¼³Á¤

°í°´´Ôµé²²´Â °¢ ±¹°¡º° ÃÖÁ¾ ¸®¼ÂÀÌ ¹ßÇ¥µÈ ÈÄ 7¿ù¿¡ ¹«·á ¾÷µ¥ÀÌÆ® ¹öÀüÀ» Á¦°øÇØ µå¸³´Ï´Ù. ÃÖÁ¾ ¾÷µ¥ÀÌÆ® ¹öÀü¿¡´Â ¸íÈ®ÇÏ°Ô Á¤ÀÇµÈ °ü¼¼ ¿µÇ⠺м®ÀÌ Æ÷ÇԵǾî ÀÖ½À´Ï´Ù.

»óÈ£ ¹× ¾çÀÚ °£ ¹«¿ª°ú °ü¼¼ÀÇ ¿µÇ⠺м® :

¹Ì±¹ <>& Áß±¹ <>& ¸ß½ÃÄÚ <>& ij³ª´Ù <>&EU <>& ÀϺ» <>& Àεµ <>& ±âŸ 176°³±¹

¾÷°è ÃÖ°íÀÇ ÀÌÄÚ³ë¹Ì½ºÆ®: Global Industry AnalystsÀÇ Áö½Ä ±â¹ÝÀº ±¹°¡, ½ÌÅ©ÅÊÅ©, ¹«¿ª ¹× »ê¾÷ ´Üü, ´ë±â¾÷, ±×¸®°í ¼¼°è °è·® °æÁ¦ »óȲÀÇ Àü·Ê ¾ø´Â ÆÐ·¯´ÙÀÓ ÀüȯÀÇ ¿µÇâÀ» °øÀ¯ÇÏ´Â ºÐ¾ßº° Àü¹®°¡ µî °¡Àå ¿µÇâ·Â ÀÖ´Â ¼ö¼® ÀÌÄÚ³ë¹Ì½ºÆ® ±×·ìÀ» Æ÷ÇÔÇÑ 14,949¸íÀÇ ÀÌÄÚ³ë¹Ì½ºÆ®¸¦ ÃßÀûÇϰí ÀÖ½À´Ï´Ù. 16,491°³ ÀÌ»óÀÇ º¸°í¼­ ´ëºÎºÐ¿¡ ¸¶ÀϽºÅæ¿¡ ±â¹ÝÇÑ 2´Ü°è Ãâ½Ã ÀÏÁ¤ÀÌ Àû¿ëµÇ¾î ÀÖ½À´Ï´Ù.

¸ñÂ÷

Á¦1Àå Á¶»ç ¹æ¹ý

Á¦2Àå ÁÖ¿ä ¿ä¾à

Á¦3Àå ½ÃÀå ºÐ¼®

Á¦4Àå °æÀï

LSH
¿µ¹® ¸ñÂ÷

¿µ¹®¸ñÂ÷

Global Convolutional Neural Networks Market to Reach US$116.0 Billion by 2030

The global market for Convolutional Neural Networks estimated at US$14.4 Billion in the year 2024, is expected to reach US$116.0 Billion by 2030, growing at a CAGR of 41.6% over the analysis period 2024-2030. Hardware Component, one of the segments analyzed in the report, is expected to record a 45.9% CAGR and reach US$78.7 Billion by the end of the analysis period. Growth in the Software Component segment is estimated at 33.8% CAGR over the analysis period.

The U.S. Market is Estimated at US$3.8 Billion While China is Forecast to Grow at 39.2% CAGR

The Convolutional Neural Networks market in the U.S. is estimated at US$3.8 Billion in the year 2024. China, the world's second largest economy, is forecast to reach a projected market size of US$17.0 Billion by the year 2030 trailing a CAGR of 39.2% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 38.6% and 35.6% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 28.5% CAGR.

Global Convolutional Neural Networks Market - Key Trends & Drivers Summarized

Why Are Convolutional Neural Networks Transforming the Frontier of AI-Powered Vision and Recognition?

Convolutional Neural Networks (CNNs) have become foundational in advancing artificial intelligence, particularly in the realm of image and pattern recognition. Inspired by the human visual cortex, CNNs are designed to automatically and adaptively learn spatial hierarchies of features from input data, making them exceptionally effective for analyzing visual imagery. Their layered architecture-comprising convolutional layers, pooling layers, and fully connected layers-enables them to detect low-level patterns like edges and corners, and progressively identify more complex structures such as shapes, faces, or objects. CNNs are at the core of applications ranging from facial recognition and autonomous driving to medical image diagnostics and quality control in manufacturing. Their strength lies in their ability to generalize features across varying scales and orientations without the need for manual feature engineering. This makes them not only accurate but also scalable across diverse datasets. With the explosion of visual data through smartphones, surveillance systems, and industrial sensors, the demand for high-performance CNN models is growing rapidly. As more industries recognize the value of real-time visual interpretation, CNNs are evolving from academic concepts into indispensable tools driving automation, intelligence, and operational precision.

How Are Industry-Specific Applications Fueling CNN Adoption Across Sectors?

The adoption of Convolutional Neural Networks is accelerating across a wide range of industries, driven by their unique ability to extract meaningful insights from complex image and video data. In healthcare, CNNs are powering diagnostic systems that analyze X-rays, MRIs, and histopathological images to detect diseases such as cancer, tuberculosis, and neurological disorders with expert-level accuracy. In automotive, CNNs are central to the vision systems of autonomous vehicles, enabling lane detection, object recognition, pedestrian tracking, and real-time decision-making. The retail industry is using CNNs for inventory monitoring, shopper behavior analysis, and visual search, while agriculture benefits from plant disease detection, crop health monitoring, and automated harvesting powered by vision-based models. In security and defense, CNNs are used in facial recognition, surveillance video analysis, and threat detection systems. E-commerce platforms leverage CNNs for product tagging, image classification, and augmented reality (AR) shopping experiences. Even in creative sectors like fashion and media, CNNs are driving innovations in design recommendation and image-based content curation. These diverse, high-impact applications are not only expanding the footprint of CNNs but also pushing the boundaries of what AI can achieve in real-world, domain-specific contexts.

What Technological Innovations Are Expanding the Performance and Accessibility of CNNs?

Advancements in computational hardware, algorithm optimization, and AI frameworks are significantly enhancing the performance and accessibility of Convolutional Neural Networks. The development of more efficient network architectures-such as ResNet, Inception, EfficientNet, and MobileNet-has led to faster convergence, lower memory usage, and higher accuracy across tasks. These models address issues like vanishing gradients and overfitting, making CNNs more robust and generalizable. The use of transfer learning allows CNNs to be pre-trained on large datasets (like ImageNet) and fine-tuned for specific tasks with minimal data, reducing the resource burden for smaller organizations. On the hardware front, the rise of GPUs, TPUs, and edge AI chips has made it possible to train and deploy CNNs at scale, even in resource-constrained environments. Edge deployment is particularly valuable in applications requiring low latency, such as real-time surveillance or in-vehicle image processing. Meanwhile, open-source frameworks like TensorFlow, PyTorch, and Keras are democratizing access to CNN development, allowing developers, researchers, and enterprises to build and iterate rapidly. Integration with cloud platforms and AI-as-a-service models is further lowering the barrier to entry. These innovations are making CNNs more efficient, cost-effective, and applicable across an expanding spectrum of industries and use cases.

What Are the Key Factors Driving the Global Growth of Convolutional Neural Networks?

The growth in the Convolutional Neural Networks market is driven by the convergence of data availability, industry demand for automation, and continuous improvements in AI infrastructure. One of the primary drivers is the exponential increase in image and video data generated through smartphones, IoT devices, surveillance systems, and social media platforms, all of which require intelligent systems to interpret and extract value. Businesses are increasingly seeking ways to automate visual inspection, decision-making, and customer interaction-tasks that CNNs are uniquely equipped to handle. The push toward digital transformation, especially in sectors like healthcare, automotive, manufacturing, and retail, is accelerating investments in AI-based vision technologies. Meanwhile, the growing emphasis on real-time analytics and edge computing is prompting the adoption of lightweight CNN models that can run efficiently on embedded devices. Government and defense sectors are also investing in AI-driven surveillance and security solutions, further boosting demand. Additionally, the integration of CNNs with complementary technologies such as augmented reality, robotics, and natural language processing is expanding the scope and appeal of CNN-powered systems. As AI regulations mature and responsible AI practices become mainstream, CNNs will continue to evolve with improved transparency, fairness, and explainability-ensuring their relevance and trustworthiness in future applications. These combined forces are creating a strong and sustainable growth trajectory for Convolutional Neural Networks on a global scale.

SCOPE OF STUDY:

The report analyzes the Convolutional Neural Networks market in terms of units by the following Segments, and Geographic Regions/Countries:

Segments:

Component (Hardware, Software, Services); Deployment (On-Premise, Cloud); Application (Image & Video Recognition, Natural Language Processing, Medical Image Analysis, Autonomous Vehicles, Robotics & Manufacturing, Other Applications); Vertical (Healthcare, Automotive, Retail & E-Commerce, IT & Telecommunications, Manufacturing, Aerospace & Defense, Energy & Utilities, Other Verticals)

Geographic Regions/Countries:

World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; and Rest of Europe); Asia-Pacific; Rest of World.

Select Competitors (Total 42 Featured) -

TARIFF IMPACT FACTOR

Our new release incorporates impact of tariffs on geographical markets as we predict a shift in competitiveness of companies based on HQ country, manufacturing base, exports and imports (finished goods and OEM). This intricate and multifaceted market reality will impact competitors by artificially increasing the COGS, reducing profitability, reconfiguring supply chains, amongst other micro and macro market dynamics.

We are diligently following expert opinions of leading Chief Economists (14,949), Think Tanks (62), Trade & Industry bodies (171) worldwide, as they assess impact and address new market realities for their ecosystems. Experts and economists from every major country are tracked for their opinions on tariffs and how they will impact their countries.

We expect this chaos to play out over the next 2-3 months and a new world order is established with more clarity. We are tracking these developments on a real time basis.

As we release this report, U.S. Trade Representatives are pushing their counterparts in 183 countries for an early closure to bilateral tariff negotiations. Most of the major trading partners also have initiated trade agreements with other key trading nations, outside of those in the works with the United States. We are tracking such secondary fallouts as supply chains shift.

To our valued clients, we say, we have your back. We will present a simplified market reassessment by incorporating these changes!

APRIL 2025: NEGOTIATION PHASE

Our April release addresses the impact of tariffs on the overall global market and presents market adjustments by geography. Our trajectories are based on historic data and evolving market impacting factors.

JULY 2025 FINAL TARIFF RESET

Complimentary Update: Our clients will also receive a complimentary update in July after a final reset is announced between nations. The final updated version incorporates clearly defined Tariff Impact Analyses.

Reciprocal and Bilateral Trade & Tariff Impact Analyses:

USA <> CHINA <> MEXICO <> CANADA <> EU <> JAPAN <> INDIA <> 176 OTHER COUNTRIES.

Leading Economists - Our knowledge base tracks 14,949 economists including a select group of most influential Chief Economists of nations, think tanks, trade and industry bodies, big enterprises, and domain experts who are sharing views on the fallout of this unprecedented paradigm shift in the global econometric landscape. Most of our 16,491+ reports have incorporated this two-stage release schedule based on milestones.

COMPLIMENTARY PREVIEW

Contact your sales agent to request an online 300+ page complimentary preview of this research project. Our preview will present full stack sources, and validated domain expert data transcripts. Deep dive into our interactive data-driven online platform.

TABLE OF CONTENTS

I. METHODOLOGY

II. EXECUTIVE SUMMARY

III. MARKET ANALYSIS

IV. COMPETITION

(ÁÖ)±Û·Î¹úÀÎÆ÷¸ÞÀÌ¼Ç 02-2025-2992 kr-info@giikorea.co.kr
¨Ï Copyright Global Information, Inc. All rights reserved.
PC¹öÀü º¸±â