Garbage Classification Algorithm Based on Improved MobileNetV3

Abstract

In recent years, the amount of household waste has increased sharply, and there is an urgent need to use intelligent waste classification equipment to assist in completing waste classification tasks. However, existing garbage classification algorithms have large parameter counts and poor real-time performance, which are not suitable for embedded garbage classification devices. Therefore, in order to achieve a lightweight and efficient classification model, this article uses a self-built garbage dataset and a pre-trained MobileNetV3 model in the PyTorch framework for garbage recognition and classification. We introduce the CBAM attention mechanism in the network model to enhance spatial feature perception. Utilize the Mish activation function to fully utilize the extracted depth image information. Using global average pooling instead of the fully connected layer of the original model reduces the number of model parameters while improving the recognition accuracy of the model. Finally, we propose an improved lightweight model called GMC-MobileNetV3. The experimental results show that the recognition accuracy of the improved MobileNetV3 model on self-built data set reaches 96.55%, which is 3.6% higher than that of the original model, the number of model parameters is 0.64M, the memory resource consumption is reduced by 56.6%, and the recognition time of a single garbage image is only 26.4ms. The network proposed in this article can achieve low consumption and high accuracy in garbage recognition and classification, providing reference for future academic research and engineering practice.

Keywords

• Classification algorithms
• convolutional networks
• deep learning
• image recognition
• transfer learning