%0 Journal Article
%T Garbage Classification Detection Based on Improved YOLOV4
%A Qingqiang Chen
%A Qianghua Xiong
%J Journal of Computer and Communications
%P 285-294
%@ 2327-5227
%D 2020
%I Scientific Research Publishing
%R 10.4236/jcc.2020.812023
%X <div style=\"text-align:justify;\">
	As the rate of garbage generation gradually increases, the past garbage disposal methods will be eliminated, so the classification of garbage has become an inevitable choice. The multi-category classification of garbage and the accuracy of recognition have also become the focus of attention. Aiming at the problems of single category, few types of objects and low accuracy in existing garbage classification algorithms. This paper proposes to use the improved YOLOV4 network framework to detect 3 categories, a total of 15 objects, and find that the average accuracy is 64%, Frame per second 92f/s. It turns out that the improved YOLOV4 can better detect garbage categories and is suitable for embedded devices.
</div>
%K YOLOV4
%K Computer Vision
%K Classification
%K Convolutional Network
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=106526