{"id":328,"date":"2021-07-14T13:17:57","date_gmt":"2021-07-14T13:17:57","guid":{"rendered":"https:\/\/aviarsis.com\/tr\/?page_id=328"},"modified":"2021-07-15T19:23:22","modified_gmt":"2021-07-15T19:23:22","slug":"gorsel-denetim-cozumleri","status":"publish","type":"page","link":"https:\/\/aviarsis.com\/tr\/?page_id=328","title":{"rendered":"G\u00f6rsel Denetim \u00c7\u00f6z\u00fcmleri"},"content":{"rendered":"\n
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Bilgisayarl\u0131 G\u00f6r\u00fc ile G\u00f6rsel Denetim<\/h2>\n\n\n\n

Bilgisayarl\u0131 g\u00f6r\u00fc, yapay zekan\u0131n alt alanlar\u0131ndan biridir. Bu alan\u0131n odak noktalar\u0131ndan biri g\u00f6r\u00fcnt\u00fc ve videolardan nesne tespitidir. G\u00f6r\u00fcnt\u00fclerdeki nesneleri alg\u0131lamak i\u00e7in konvol\u00fcsyonel sinir a\u011flar\u0131 gibi farkl\u0131 algoritmalar kullan\u0131labilir. Ayr\u0131ca bu algoritmalar tek a\u015famal\u0131 detekt\u00f6rler ve iki a\u015famal\u0131 detekt\u00f6rler olarak s\u0131n\u0131fland\u0131r\u0131labilir. Bu algoritmalar\u0131 i\u00e7eren Tensorflow, Keras, Detectron ve YOLO gibi farkl\u0131 k\u00fct\u00fcphaneler bulunmaktad\u0131r. Bu k\u00fct\u00fcphaneler, perakendeden lojisti\u011fe kadar farkl\u0131 sekt\u00f6rlerdeki farkl\u0131 sorunlar\u0131 \u00e7\u00f6zmek i\u00e7in kullan\u0131labilir. \u00d6rne\u011fin, perakende sekt\u00f6r\u00fcnde belirlenen lokasyonda kamera kullan\u0131larak ki\u015filer say\u0131labilir veya bo\u015f raflar tespit edilebilir.<\/p>\n\n\n\n

Global olarak farkl\u0131 sekt\u00f6rler i\u00e7in makine \u00f6\u011frenmesi ve g\u00f6r\u00fcnt\u00fc tan\u0131ma yakla\u015f\u0131mlar\u0131n\u0131 kullanarak yaz\u0131l\u0131m projeleri ve \u00e7\u00f6z\u00fcmleri geli\u015ftirme kabiliyetine sahibiz.<\/p>\n\n\n\n

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Aviarsis Rafta Bulunabilirlik \u00c7\u00f6z\u00fcm\u00fc<\/h2>\n\n\n\n

Kalan \u00fcr\u00fcnler, geleneksel envanter y\u00f6netimi sistemleri kullan\u0131larak kontrol edilebilir, ancak yaln\u0131zca stoktaki \u00fcr\u00fcn say\u0131s\u0131n\u0131 verirler. Stokta bulunan bu \u00fcr\u00fcnler raflarda olmayabilir. Mevcut envanter sistemleri raflardaki \u00fcr\u00fcn say\u0131s\u0131n\u0131 anlayamaz. Rafta kullan\u0131labilirlik (OSA), marketlerin \u00e7o\u011funda manuel kontrol ile \u00e7al\u0131\u015fanlar taraf\u0131ndan sa\u011flan\u0131r. Bu yakla\u015f\u0131m etkili de\u011fildir ve i\u015f g\u00fcc\u00fc gerektirir. Aviarsis OSA \u00e7\u00f6z\u00fcm\u00fc, g\u00f6r\u00fcnt\u00fc tan\u0131ma ve nesne alg\u0131lama tekniklerini kullanarak bo\u015f raflar\u0131, bitmek \u00fczere olan raflar\u0131 ve raflardaki \u00fcr\u00fcnleri alg\u0131lar. Rafta bulunurluk bu sayede y\u00fckseltilebilir. \u00c7\u00f6z\u00fcm\u00fcm\u00fcz, h\u0131zl\u0131 t\u00fcketim \u00fcr\u00fcnleri (FMCG) gibi se\u00e7ilen reyonlar i\u00e7in uygulanabilir. Ayr\u0131ca, \u00e7\u00f6z\u00fcm\u00fcm\u00fcz m\u00fc\u015fterinin ihtiya\u00e7lar\u0131na g\u00f6re tamamen \u00f6zelle\u015ftirilebilir. Tespit sonu\u00e7lar\u0131n\u0131 raporlar ve ERP, SAP vb. mevcut sistemlere entegrasyon se\u00e7enekleri sunar.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Aviarsis Personel Koruyucu Ekipman Kontrol \u00c7\u00f6z\u00fcm\u00fc<\/h2>\n\n\n\n

\u00c7\u00f6z\u00fcm\u00fcm\u00fcz, g\u00f6r\u00fcnt\u00fc tan\u0131ma ve nesne alg\u0131lama tekniklerini kullanarak baret ve g\u00f6zl\u00fck gibi koruyucu ekipmanlar\u0131 tespit eder. Aviarsis PPE \u00e7\u00f6z\u00fcm\u00fc, \u00e7al\u0131\u015fanlar i\u00e7in i\u015fyeri g\u00fcvenli\u011fini art\u0131rmaya yard\u0131mc\u0131 olur. Ayr\u0131ca, \u00e7\u00f6z\u00fcm\u00fcm\u00fcz ihtiya\u00e7lar\u0131n\u0131za g\u00f6re tamamen \u00f6zelle\u015ftirilebilir. Tespit sonu\u00e7lar\u0131n\u0131 raporlar ve ERP, SAP vb. mevcut sistemlere entegrasyon se\u00e7enekleri sunar.<\/p>\n\n\n\n

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A\u00e7\u0131klanabilir Yapay Zeka<\/h2>\n\n\n\n

Makine \u00f6\u011frenimi ve yapay zeka tabanl\u0131 uygulamalar\u0131n \u00e7al\u0131\u015fma ve karar verme mant\u0131\u011f\u0131n\u0131n bu alanlarda uzman olmayan kullan\u0131c\u0131lar taraf\u0131ndan anla\u015f\u0131lmas\u0131 zordur. A\u00e7\u0131klanabilir yapay zeka (XAI) yakla\u015f\u0131m\u0131, kullan\u0131c\u0131lar\u0131n yapay zeka tabanl\u0131 uygulamalara g\u00fcvenmelerine, anlamalar\u0131na ve y\u00f6netmelerine yard\u0131mc\u0131 olur. Bu \u015fekilde, modellerini bir uzman olmadan de\u011fi\u015fikliklere uyarlamak i\u00e7in veri k\u00fcmelerini g\u00fcncelleyebilirler. M\u00fc\u015fterinin ihtiya\u00e7lar\u0131na g\u00f6re XAI yakla\u015f\u0131m\u0131yla desteklenen yapay zeka \u00e7\u00f6z\u00fcmleri sunuyoruz.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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