Research on Automatic Garbage Detection System Based on Deep Learning and Narrowband Internet of Things

Abstract

This paper proposes an automatic garbage detection system based on deep learning and narrowband Internet of things. The system automatically detects and identifies decoration garbage directly in front-end embedded monitoring module, and manages thousands of monitoring front-ends through narrow-band Internet of Things and background server. In the front-end embedded module of the system, the improved YOLOv2 network model is adopted to do garbage detection and recognition. Means of target box dimension clustering and classification network pre-training are used to improve the YOLOv2 model performance; at the same time, the network is lightweight by replacing the feature extraction network and other methods, and the lightweight YOLOv2 network is optimized and ported to embedded module. The experiment test shows that compared with the traditional monitoring system, the cost is reduced by more than half, which can effectively save manpower and material resources, and the accuracy of detection and recognition has also been improved.