{"id":406,"date":"2019-05-10T19:22:32","date_gmt":"2019-05-10T19:22:32","guid":{"rendered":"http:\/\/mrsdprojects.ri.cmu.edu\/2018teamc\/?p=406"},"modified":"2019-05-11T19:23:10","modified_gmt":"2019-05-11T19:23:10","slug":"block-detection-and-classification-with-yolov2","status":"publish","type":"post","link":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teamc\/2019\/05\/10\/block-detection-and-classification-with-yolov2\/","title":{"rendered":"Block Detection and Classification with YOLOv2"},"content":{"rendered":"<p>In order to detect and classify the blocks in the field of view of the camera,<br \/>\nwe will be using the YOLOv2 deep neural network. This networks takes<\/p>\n<p>as input RGB frames from the realsense, and regresses the bounding box lo-<br \/>\ncation and size. It also provides the bounding box class (in our case one of 3<\/p>\n<p>classes: Red, Blue, Green), and a confidence measure of the detection. A big<br \/>\nadvantage is that it works in real time while providing competent prediction<br \/>\nand classification accuracy.<br \/>\n<img loading=\"lazy\" decoding=\"async\" src=\"http:\/\/mrsdprojects.ri.cmu.edu\/2018teamc\/wp-content\/uploads\/sites\/33\/2019\/05\/predictionyolo-1024x325.png\" alt=\"\" width=\"1024\" height=\"325\" class=\"alignnone size-large wp-image-407\" srcset=\"https:\/\/mrsdprojects.ri.cmu.edu\/2018teamc\/wp-content\/uploads\/sites\/33\/2019\/05\/predictionyolo-1024x325.png 1024w, https:\/\/mrsdprojects.ri.cmu.edu\/2018teamc\/wp-content\/uploads\/sites\/33\/2019\/05\/predictionyolo-300x95.png 300w, https:\/\/mrsdprojects.ri.cmu.edu\/2018teamc\/wp-content\/uploads\/sites\/33\/2019\/05\/predictionyolo-768x244.png 768w, https:\/\/mrsdprojects.ri.cmu.edu\/2018teamc\/wp-content\/uploads\/sites\/33\/2019\/05\/predictionyolo.png 1148w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In order to detect and classify the blocks in the field of view of the camera, we will be using the YOLOv2 deep neural network. This networks takes as input RGB frames from the realsense, and regresses the bounding box lo- cation and size. It also provides the bounding box class (in our case one<br \/><a class=\"moretag\" href=\"https:\/\/mrsdprojects.ri.cmu.edu\/2018teamc\/2019\/05\/10\/block-detection-and-classification-with-yolov2\/\">+ Read More<\/a><\/p>\n","protected":false},"author":148,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3],"tags":[],"class_list":["post-406","post","type-post","status-publish","format-standard","hentry","category-cv"],"_links":{"self":[{"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teamc\/wp-json\/wp\/v2\/posts\/406","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teamc\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teamc\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teamc\/wp-json\/wp\/v2\/users\/148"}],"replies":[{"embeddable":true,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teamc\/wp-json\/wp\/v2\/comments?post=406"}],"version-history":[{"count":2,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teamc\/wp-json\/wp\/v2\/posts\/406\/revisions"}],"predecessor-version":[{"id":409,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teamc\/wp-json\/wp\/v2\/posts\/406\/revisions\/409"}],"wp:attachment":[{"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teamc\/wp-json\/wp\/v2\/media?parent=406"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teamc\/wp-json\/wp\/v2\/categories?post=406"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mrsdprojects.ri.cmu.edu\/2018teamc\/wp-json\/wp\/v2\/tags?post=406"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}