A YOLO-based Neural Network with VAE for Intelligent Garbage Detection and Classification

Garbage recycling is becoming an urgent need for the people as the rapid development of human society is producing colossal amount of waste every year. However, current machine learning models for intelligent garbage detection and classification are highly constrained by their limited processing speeds and large model sizes, which make them difficult to be deployed on portable, real-time, and energy-efficient edge-computing devices. Therefore, in this paper, we introduce a novel YOLO-based neural network model with Variational Autoencoder (VAE) to increase the accuracy of automatic garbage recycling, accelerate the speed of calculation, and reduce the model size to make it feasible in the real-world garbage recycling scenario. The model is consisted of a convolutional feature extractor, a convolutional predictor, and a decoder. After the training process, this model achieves a correct rate of 69.70% with a total number of 32.1 million parameters and a speed of processing 60 Frames Per Second (FPS), surpassing the performance of other existing models such as YOLO v1 and Fast R-CNN.