{"id":2841,"date":"2021-09-08T17:47:32","date_gmt":"2021-09-08T12:17:32","guid":{"rendered":"https:\/\/www.datalabelify.com\/?p=2841"},"modified":"2023-11-03T11:50:03","modified_gmt":"2023-11-03T06:20:03","slug":"computer-vision-opportunities-and-challenges","status":"publish","type":"post","link":"https:\/\/www.datalabelify.com\/tr\/bilgisayar-goru-firsatlar-ve-zorluklar\/","title":{"rendered":"Bilgisayarla G\u00f6r\u00fc: F\u0131rsatlar ve Zorluklar"},"content":{"rendered":"
Bilgisayarla G\u00f6r\u00fc: F\u0131rsatlar ve Zorluklar<\/em><\/h6>\n

Sekt\u00f6rlerde kullan\u0131lan yapay zeka (AI), oyunun kurallar\u0131n\u0131 de\u011fi\u015ftiren i\u00e7g\u00f6r\u00fclere ve yeni \u00fcr\u00fcnlerin olu\u015fturulmas\u0131na olanak tan\u0131r. Ayr\u0131ca karma\u015f\u0131k g\u00f6revleri otomatikle\u015ftirir. B\u00fcy\u00fck miktarda g\u00f6rsel veri \u00fcreten end\u00fcstrileri d\u00f6n\u00fc\u015ft\u00fcrmek i\u00e7in b\u00fcy\u00fck potansiyele sahip bir yapay zeka uygulamas\u0131, bilgisayar g\u00f6r\u00fc\u015f\u00fcd\u00fcr.<\/p>\n

Bilgisayar g\u00f6r\u00fc\u015f\u00fc kullan\u0131m durumlar\u0131, di\u011fer bir\u00e7ok kullan\u0131m durumuyla birlikte k\u00f6pek e\u011fitimi ve hayat kurtarma aras\u0131nda de\u011fi\u015febilir. Bunlar\u0131 yaratmak iki y\u00f6nl\u00fc bir zorluktur. Ek a\u00e7\u0131klama y\u00f6ntemlerinizi (video, s\u0131n\u0131rlay\u0131c\u0131 kutu, \u00e7okgen) ve modelinizin tan\u0131mas\u0131n\u0131 istedi\u011finiz nesneleri, hedefleri veya davran\u0131\u015flar\u0131 se\u00e7ebilirsiniz.<\/p>\n

Makineyi g\u00f6rsel olarak tan\u0131mas\u0131 i\u00e7in e\u011fitmek i\u00e7in gereken b\u00fcy\u00fck miktarda veriyi do\u011fru \u015fekilde etiketlemek.<\/p>\n

Bu, \u00f6zellikle g\u00f6rsel verileriniz olarak \u00e7oklu \u00e7er\u00e7eve veya videolar\u0131n\u0131z varsa ge\u00e7erlidir.<\/p>\n

Video verilerine a\u00e7\u0131klama eklemek, \u00e7e\u015fitli uygulamalarda \u00e7ok kullan\u0131\u015fl\u0131d\u0131r. A\u00e7\u0131klamal\u0131 G\u00f6r\u00fcnt\u00fc \u0130\u015fleme, otonom ara\u00e7 sistemlerini sokak s\u0131n\u0131rlar\u0131n\u0131 tan\u0131mak ve \u015ferit \u00e7izgilerini alg\u0131lamak \u00fczere e\u011fitmek i\u00e7in kullan\u0131labilir. T\u0131bbi AI i\u00e7in hastal\u0131klar\u0131 tan\u0131mlamak ve cerrahi yard\u0131m sa\u011flamak i\u00e7in kullan\u0131l\u0131r. M\u00fc\u015fterilerin yaln\u0131zca yanlar\u0131nda getirdikleri \u00fcr\u00fcnler i\u00e7in \u00fccretlendirildi\u011fi, \u00f6deme gerektirmeyen perakende sat\u0131\u015f ortamlar\u0131 olu\u015fturmak i\u00e7in de kullan\u0131labilir. \u0130lgin\u00e7 bir uygulama, bilim adamlar\u0131n\u0131n g\u00fcne\u015f enerjisi teknolojisinin ku\u015flar \u00fczerindeki etkileri hakk\u0131nda daha fazla bilgi edinmelerini sa\u011flayan verimli bir sistem olu\u015fturmak i\u00e7in kullan\u0131labilen video a\u00e7\u0131klamad\u0131r.<\/p>\n

<\/h3>\n

Video Ek A\u00e7\u0131klamas\u0131: Ne \u0130\u015fe Yarar?<\/h3>\n

Video ek a\u00e7\u0131klamas\u0131, bir alt k\u00fcme g\u00f6r\u00fcnt\u00fc ek a\u00e7\u0131klamas\u0131 olarak kabul edilebilir ve ayn\u0131 ara\u00e7lar\u0131n \u00e7o\u011funu kullan\u0131r. Ancak s\u00fcre\u00e7 daha karma\u015f\u0131kt\u0131r. Videolar i\u00e7in ek a\u00e7\u0131klama i\u015flemi saniyede 60 kareye kadar s\u00fcrebilir. Bu, resimlere a\u00e7\u0131klama eklemekten \u00e7ok daha uzun s\u00fcrebilece\u011fi anlam\u0131na gelir.<\/p>\n

Videoya iki \u015fekilde a\u00e7\u0131klama ekleyebilirsiniz:<\/span><\/p>\n

Video a\u00e7\u0131klamas\u0131 i\u00e7in orijinal y\u00f6ntem tek karedir. Annotator, videoyu bir\u00e7ok g\u00f6r\u00fcnt\u00fcye b\u00f6ler ve her seferinde bir a\u00e7\u0131klama ekler. Bu bazen \u00e7er\u00e7eveden \u00e7er\u00e7eveye bir kopya ek a\u00e7\u0131klaman\u0131n yard\u0131m\u0131yla ger\u00e7ekle\u015ftirilebilir. Bu verimsiz ve zaman al\u0131c\u0131d\u0131r. Bu, nesnelerin \u00e7er\u00e7eveler i\u00e7inde daha az dinamik oldu\u011fu belirli durumlarda i\u015fe yarayabilir.<\/p>\n

Video ak\u0131\u015f\u0131 daha pop\u00fcler. Ek a\u00e7\u0131klama yapan ki\u015fi, veri a\u00e7\u0131klama arac\u0131n\u0131n \u00f6zelle\u015ftirilmi\u015f \u00f6zelliklerini kullanarak periyodik olarak a\u00e7\u0131klamalar yapar. Bu daha h\u0131zl\u0131d\u0131r ve a\u00e7\u0131klay\u0131c\u0131, nesneleri \u00e7er\u00e7eve i\u00e7inde hareket ederken g\u00f6sterebilir. Bu, daha iyi makine \u00f6\u011frenimine yol a\u00e7abilir. Veri ek a\u00e7\u0131klama ara\u00e7lar\u0131 pazar\u0131 b\u00fcy\u00fcd\u00fck\u00e7e ve sa\u011flay\u0131c\u0131lar ara\u00e7 platformu yeteneklerini geni\u015flettik\u00e7e bu y\u00f6ntem daha h\u0131zl\u0131 ve daha yayg\u0131n hale geliyor.<\/p>\n

\u0130zleme, nesnelerin hareketlerine a\u00e7\u0131klama ekleme y\u00f6ntemidir. Enterpolasyon, baz\u0131 g\u00f6r\u00fcnt\u00fc a\u00e7\u0131klama ara\u00e7lar\u0131n\u0131n bir \u00f6zelli\u011fidir ve bir anlat\u0131c\u0131n\u0131n bir \u00e7er\u00e7eveyi etiketlemesine ve ard\u0131ndan ba\u015fka bir \u00e7er\u00e7eveye atlamas\u0131na olanak tan\u0131r. Bu, anlat\u0131c\u0131n\u0131n notu nesnenin daha sonra g\u00f6r\u00fcnd\u00fc\u011f\u00fc konuma ta\u015f\u0131mas\u0131na olanak tan\u0131r.<\/p>\n

Enterpolasyon, hareketi doldurmak ve nesnenin hareketlerini aralar\u0131nda a\u00e7\u0131klama eklenmemi\u015f \u00e7er\u00e7evelerde izlemek (veya enterpolasyon yapmak) i\u00e7in makine \u00f6\u011frenimini kullan\u0131r.<\/p>\n

Bir bilgisayar g\u00f6r\u00fc\u015f\u00fc olu\u015fturmak istiyorsan\u0131z modeli<\/a> Ameliyat s\u0131ras\u0131nda bir ne\u015fteri kontrol etme yetene\u011fine sahipseniz, binlerce veya y\u00fczlerce farkl\u0131 cerrahi prosed\u00fcrden ne\u015fter hareketlerini g\u00f6steren a\u00e7\u0131klamal\u0131 videolar kullanman\u0131z gerekecektir. Bu videolar, makineyi bir ne\u015fterin nas\u0131l tan\u0131naca\u011f\u0131n\u0131 ve izlenece\u011fini e\u011fitmek i\u00e7in kullan\u0131labilir.<\/p>\n

\u0130\u015fg\u00fcc\u00fc, G\u00f6r\u00fcnt\u00fc \u0130\u015fleme i\u00e7in kritik bir se\u00e7imdir<\/h4>\n

Video ek a\u00e7\u0131klamas\u0131, i\u015f g\u00fcc\u00fcn\u00fcz\u00fc etkileyecek bir karard\u0131r. Bilgisayarla g\u00f6rme modelleri olu\u015fturulurken i\u015f g\u00fcc\u00fcn\u00fcn \u00f6nemli bir husus oldu\u011fu genellikle g\u00f6z ard\u0131 edilir. Ancak, projenin ba\u015f\u0131ndan itibaren daha stratejik olarak d\u00fc\u015f\u00fcn\u00fclmelidir.<\/p>\n

Bilgisayarla g\u00f6rme modellerini e\u011fitmek i\u00e7in gereken b\u00fcy\u00fck miktarda veri nedeniyle kurum i\u00e7i a\u00e7\u0131klay\u0131c\u0131lar\u0131 \u00f6l\u00e7eklendirmek zor olabilir. Ayr\u0131ca \u00f6nemli bir y\u00f6netim gerektirirler. Kitle kaynak kullan\u0131m\u0131, b\u00fcy\u00fck ek a\u00e7\u0131klama ekiplerine h\u0131zl\u0131 bir \u015fekilde kaynak sa\u011flaman\u0131n pop\u00fcler bir yoludur, ancak \u00e7al\u0131\u015fanlar do\u011fruluklar\u0131ndan sorumlu olmad\u0131\u011f\u0131ndan ve daha az g\u00fcvenilir olabilece\u011finden kalite sorunlar\u0131na neden olabilir.<\/p>\n

Profesyonelce y\u00f6netilen ek a\u00e7\u0131klama ekipleri, \u00f6zellikle son derece do\u011fru ortamlarda \u00e7al\u0131\u015fan makine \u00f6\u011frenimi modelleri olu\u015ftururken m\u00fckemmel bir se\u00e7imdir. Zaman i\u00e7inde, yorumlay\u0131c\u0131lar\u0131n i\u015f kurallar\u0131n\u0131z ve son durumlar\u0131n\u0131z hakk\u0131ndaki bilgileri geli\u015fir ve bu da daha y\u00fcksek kaliteli verilere ve daha verimli bilgisayar g\u00f6rme modellerine yol a\u00e7ar.<\/p>\n

Daha da iyisi, ekibiniz yak\u0131n ileti\u015fim ile sizin bir uzant\u0131n\u0131z gibi \u00e7al\u0131\u015fmal\u0131d\u0131r. Bu, modellerinizi e\u011fitirken, do\u011frularken ve test ederken i\u015f ak\u0131\u015f\u0131n\u0131zda ayarlamalar yapman\u0131za olanak tan\u0131r.<\/p>\n

Labelify<\/a>: Se\u00e7ti\u011finiz Video Ek A\u00e7\u0131klama Arac\u0131<\/p>\n

Labelify, 2019'dan beri profesyonel olarak y\u00f6netilen veri analistleri ekipleri sa\u011fl\u0131yor. \u0130\u015f g\u00fcc\u00fcm\u00fcz, d\u00fcnya \u00e7ap\u0131ndaki 7 otonom ara\u00e7 \u015firketi i\u00e7in makine \u00f6\u011frenimi ve derin \u00f6\u011frenme e\u011fitimi i\u00e7in g\u00f6rsel verileri a\u00e7\u0131kl\u0131yor.<\/p>\n

Labelify'\u0131n bilgisayar g\u00f6r\u00fc\u015f\u00fc i\u00e7in video notu hakk\u0131nda daha fazla bilgi edinmek i\u00e7in bug\u00fcn bize ula\u015f\u0131n.<\/p>","protected":false},"excerpt":{"rendered":"

Computer Vision: Opportunities and Challenges Artificial intelligence (AI), which is used across industries, allows for game-changing insights and the creation of new products. It also automates complex tasks. One application of AI that has great potential to transform industries that produce large amounts of visual data is computer vision. Computer vision use cases can range […]<\/p>","protected":false},"author":3,"featured_media":14339,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[16,17,1,8],"tags":[],"class_list":["post-2841","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-computer-vision","category-data-annotation","category-video-annotation"],"blocksy_meta":[],"featured_image_urls":{"full":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/09\/Computer-Vision.jpg",2240,1260,false],"thumbnail":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/09\/Computer-Vision-150x150.jpg",150,150,true],"medium":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/09\/Computer-Vision-300x169.jpg",300,169,true],"medium_large":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/09\/Computer-Vision-768x432.jpg",768,432,true],"large":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/09\/Computer-Vision-1024x576.jpg",1024,576,true],"1536x1536":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/09\/Computer-Vision-1536x864.jpg",1536,864,true],"2048x2048":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/09\/Computer-Vision-2048x1152.jpg",2048,1152,true],"trp-custom-language-flag":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/09\/Computer-Vision-18x10.jpg",18,10,true],"ultp_layout_landscape_large":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/09\/Computer-Vision-1200x800.jpg",1200,800,true],"ultp_layout_landscape":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/09\/Computer-Vision-870x570.jpg",870,570,true],"ultp_layout_portrait":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/09\/Computer-Vision-600x900.jpg",600,900,true],"ultp_layout_square":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/09\/Computer-Vision-600x600.jpg",600,600,true],"yarpp-thumbnail":["https:\/\/www.datalabelify.com\/wp-content\/uploads\/2021\/09\/Computer-Vision-120x120.jpg",120,120,true]},"post_excerpt_stackable":"

Computer Vision: Opportunities and Challenges Artificial intelligence (AI), which is used across industries, allows for game-changing insights and the creation of new products. It also automates complex tasks. One application of AI that has great potential to transform industries that produce large amounts of visual data is computer vision. Computer vision use cases can range from dog training and life-saving, with many other use cases. It is a two-fold challenge to create them. You can choose your annotation methods (video, bounding box, polygon) and the objects, targets, or behaviors that you want your model to recognize. Correctly labeling the huge…<\/p>\n","category_list":"Artificial intelligence<\/a>, Computer Vision<\/a>, Data Annotation<\/a>, Video Annotation<\/a>","author_info":{"name":"Parth P","url":"https:\/\/www.datalabelify.com\/tr\/author\/soeuidhae\/"},"comments_num":"0 comments","_links":{"self":[{"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/posts\/2841","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/comments?post=2841"}],"version-history":[{"count":4,"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/posts\/2841\/revisions"}],"predecessor-version":[{"id":3106,"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/posts\/2841\/revisions\/3106"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/media\/14339"}],"wp:attachment":[{"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/media?parent=2841"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/categories?post=2841"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.datalabelify.com\/tr\/wp-json\/wp\/v2\/tags?post=2841"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}