Classification algorithm of garbage images based on novel spatial attention mechanism and transfer learning

As governments at all levels in China have started to promote mandatory garbage classification, in order to meet the standardization and automated garbage classification in all aspects of classification and recycling need a fine-grained image classification model suitable for cloud deployment with high accuracy and low latency. This article takes advantage of deep transfer learning to establish an end-to-end transfer learning network architecture GANet (garbage neural network). Aiming at the challenges of category confusion and background interference in garbage classification, this paper proposes a new pixel-level spatial attention mechanism PSATT (pixel-level spatial attention). In order to overcome the challenges of multi-class and sample imbalance, this paper proposes a label smoothing regularization loss function. In order to improve convergence speed, model stability and generalization, this paper proposes a stepped OneCycle learning rate control method, and gives a combined use strategy combining Rectified Adam (RAdam) optimization method and stochastic weight averaging. Experiments used the training data which are marked by the Shenzhen garbage classification standard and provided by the "Huawei cloud artificial intelligence competition • garbage classification challenge cup", and verified the significant effect of GANet in the garbage classification problem, and won the national second prize (2nd place). At the same time, the proposed PSATT mechanism is superior to the comparison methods with improvement on different backbone network architectures, and has good versatility. The GANet architecture, PSATT mechanism and training strategies proposed in this paper not only have important engineering reference value, but also have good academic value. © 2021, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.