RMGCS: Real-time Multimodal Garbage Classification System for Recyclability
Abstract
Management of garbage classification is a general term for a series of activities to sort, store and transport garbage into public resources according to certain regulations or standards. Current garbage classification systems have several drawbacks, such as inability to identify multiple garbage categories, and high dependence on the surrounding environment. To address these issues, this paper has proposed the Real Time Multi-Modal Garbage classification System (abbreviated as RMGCS). It consists of two sub systems: an indoor garbage classification applet (abbreviated as IGCA) and an outdoor garbage classification system (abbreviated as OGCS). IGCA provides users with three methods of garbage classification, and OGCS provides users with outdoor real-time multi-target garbage classification and can dynamically update the recognition model. RMGCS achieves real-time, accurate, and multimodal classification. Finally, the experiments with RMGCS show that our approaches are effective and efficient.
Contributions
1 Indoor Garbage classification Applet (abbreviate as IGCA)
Neumorphism UI of Indoor Garbage Classification Applet. The system is designed in neo-mimetic style and requires no learning and installation costs. Based on WeChat APP (over 1 billion monthly active users), it can spread quickly among users and increase its influence. This application model can effectively increase users' willingness to participate in garbage separation.
Hybrid garbage classification with named entities. The system has three garbage recognition modes, namely keyword recognition, speech recognition and image recognition. In keyword recognition and speech recognition, name entity recognition and word segmentation techniques are used to extract the noun information input by users. In the image recognition algorithm for garbage classification, this system uses an improved Resnet50 model and introduces pyramidal convolution, which effectively improves the recognition accuracy.
Separation of front and back-end deployment model.In terms of system architecture, we implement the pyramid convolution of image recognition algorithm based on tensorflow.js at the front end, and send the features to the back end after the image extraction, which not only effectively reduces the size of data sent from the front end to the back end, but also protects the data privacy of users to a certain extent.
2 Outdoor Garbage classification System (abbreviate as OGCS)
Neumorphism UI of Indoor Garbage Classification Applet. The system is designed in neo-mimetic style and requires no learning and installation costs. Based on WeChat APP (over 1 billion monthly active users), it can spread quickly among users and increase its influence. This application model can effectively increase users' willingness to participate in garbage separation.
Hybrid garbage classification with named entities. The system has three garbage recognition modes, namely keyword recognition, speech recognition and image recognition. In keyword recognition and speech recognition, name entity recognition and word segmentation techniques are used to extract the noun information input by users. In the image recognition algorithm for garbage classification, this system uses an improved Resnet50 model and introduces pyramidal convolution, which effectively improves the recognition accuracy.
Separation of front and back-end deployment model. In terms of system architecture, we implement the pyramid convolution of image recognition algorithm based on tensorflow.js at the front end, and send the features to the back end after the image extraction, which not only effectively reduces the size of data sent from the front end to the back end, but also protects the data privacy of users to a certain extent.
