TITLE:
Garbage Classification Detection Based on Improved YOLOV4
AUTHORS:
Qingqiang Chen, Qianghua Xiong
KEYWORDS:
YOLOV4, Computer Vision, Classification, Convolutional Network
JOURNAL NAME:
Journal of Computer and Communications,
Vol.8 No.12,
December
31,
2020
ABSTRACT: 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.