A Scaling Factor Based Image Processing Strategy for Object Detection

Abstract

Classified management of domestic garbage is conducive to controlling pollution, protecting the environment, saving resources and achieving sustainable urban development. To automate domestic garbage classification and improve classification rate and processing capacity, this paper innovatively proposes an image processing strategy to detect domestic garbage objects using domestic garbage images as a dataset and YOLOv5 network. The network is then fine-tuned to achieve object detection of domestic garbage. Experimental results show that after using the image processing strategy, mAP@.5:.95 of the first-class (4-class) and second-class (104-class) networks on the basic test set is increased from 15.4% and 10.9% to 28.4% and 18.5%, respectively. This demonstrates the feasibility and effectiveness of the proposed image processing strategy. In addition, the image processing strategies presented in this paper have the potential to be applied in the domain of video recognition, including Sign Language Translation and Lip-reading Recognition.