Turning Trash into Treasure: Developing an Intelligent Bin for Plastic Bottle Recycling

• Published in: Journal of social computing Vol. 5; no. 1; pp. 1 - 14
• Main Authors: Munira, Sirajam, Paul, Niloy, Alam, Md Ashraful, Rahman, Md. Mahmudur, Kaiser, M. Shamim
• Format: Journal Article
• Language: English
• Published: Tsinghua University Press 01.03.2024
• Subjects: business model; deep learning; reward system; smart environment; yolov5
• ISSN: 2688-5255; 2688-5255
• DOI: 10.23919/JSC.2024.0001

Plastic pollution has emerged as a major global concern due to its enduring nature and limited recycling options. In response to this critical challenge, this paper presents a novel approach utilizing a Detection-Based Reward System (DBRS) alongside an innovative business model to promote effective plastic waste management, reduce plastic waste accumulation in the nature, and uphold environmental cleanliness. Leveraging the YOLOv5 algorithm for its exceptional accuracy, speed, and open-source availability, plastic bottle detection becomes a pivotal aspect of this system. Users seamlessly enroll in the system, triggering an automated detection process that computes reward points corresponding to their deposited plastic bottles. These reward points are meticulously stored within a centralized database. Beyond its operational facets, this comprehensive system encompasses a robust business model, strategically poised to capture widespread engagement with waste disposal practices, thereby contributing to the realization of Sustainable Development Goals (SDGs) geared towards fostering a healthier environment. Notably, the DBRS attains cutting-edge performance in plastic bottle detection, boasting an impressive mean Average Precision (mAP) of 0.973, underscoring its efficacy in tackling plastic pollution.