Intelligent garbage detection system based on neural networks
Can Zhang, Xu Zhang, Dawei Tu, Ying Wang · 2021
It is very important to improve the purity of classified waste from the source for the recycling of waste resources and the protection of ecological environment. However, the current garbage sorting sites often require manual visual inspection, which is labor-intensive and unreliable. Recent achievements in convolutional neural network make deep learning-based visual detection technology provide a new way for intelligent development of cities. In this paper, a systematic design of the intelligent garbage detecting system and corresponding workflow are implemented to reduce labor costs. An improved Cascade RCNN algorithm, which can greatly improve the detection accuracy, is proposed for garbage detection of this intelligent device. Furthermore, in order to solve the problem that some types of garbage data sets are less, we use generative adversarial network to implement data augmentation. The experimental results show that compared with original Cascade RCNN and other commonly used target detection networks, our proposed method performs better at garbage detection.