Intelligent garbage classification system based on improve MobileNetV3-Large

Abstract

In response to the call for implementing national waste classification, this paper proposes an intelligent waste classification system based on the improved MobileNetV3-Large, which can raise the national awareness of waste classification through the combination of software and hardware. The software module is based on WeChat applet and offers functions for image recognition, text recognition, speech recognition, points-based quiz and so on. The hardware module is based on Raspberry Pi and covers image shooting, image recognition, automatic classification with automatic announcement and so on. The algorithm model applied to the image classification adopts a network model based on MobileNetV3-Large. This network model is enabled to classify garbage images through deep separable convolution, inverse residual structure, lightweight attention structure and the hard_ swish activation function. The text classification model adopts a network model based on LSTM, extracts text features through word embedding, enhancing the effect of garbage text classification. After testing, the system can leverage deep learning to realise intelligent garbage classification. The image recognition accuracy of the algorithm model was found to reach 81%, while the text recognition accuracy was as high as 97.61%.

KEYWORDS:

• Deep learning
• garbage classification
• MobileNetV3-Large
• LSTM
• combination of software and hardware

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Supported by program for scientific research start-up funds of Guangdong Ocean University [grant number R20049]. Supported by program for the National Natural Science Foundation of China [grant number 62066040]. Tongren University.