Design and Research of an AI Intelligent Paid Waste Classification Recycling Bin Based on Image Recognition

Abstract

With the acceleration of globalization and urbanization, waste management has become a major environmental challenge. In China alone, the amount of municipal solid waste exceeded 250 million tons in 2023, while the recycling rate remained as low as 20%, highlighting the urgent need for efficient classification solutions. This paper proposes the design of an AI-powered intelligent paid waste-sorting and recycling bin based on image recognition, aiming to address the inefficiency, low participation, and high maintenance costs of traditional recycling bins. The system integrates AI image recognition technology, sensor modules, and an incentive mechanism, achieving a classification accuracy of over 95% through the YOLO algorithm, while raising resident participation rates from 18% to 63% through financial incentives. Starting from the technical principles, the study elaborates on the system architecture, covering both hardware (e.g., camera, controller, sensors) and software (edge AI and mobile application) design. A simulated case study in a community scenario verified its potential, showing a 38% increase in recyclables collected and a reduction of 1.2 tons of carbon emissions per ton of waste. Performance comparisons demonstrated that the proposed system outperforms traditional approaches in terms of accuracy, participation, and cost-effectiveness. Despite limitations such as sensitivity to lighting conditions and cost fluctuations, future improvements can be achieved through multi-sensor fusion, blockchain-based traceability, and open-source hardware optimization. This research provides an innovative framework for AI-driven sustainable waste management, supports the United Nations Sustainable Development Goals, and contributes to the construction of “zero-waste cities.”