Real time intelligent garbage monitoring and efficient collection using Yolov8 and Yolov5 deep learning models for environmental sustainability

作者：M. Abo-Zahhad, M. Abo-Zahhad · 发表于：Scientific Reports · 年份：2025 · DOI：10.1038/s41598-025-99885-x · 被引用次数：21 · 研究领域：Medicine

Effective waste management is currently one of the most influential factors in enhancing the quality of life. Increased garbage production has been identified as a significant problem for many cities worldwide and a crucial issue for countries experiencing rapid urban population growth. According to the World Bank Organization, global waste production is projected to increase from 2.01 billion tonnes in 2018 to 3.4 billion tonnes by 2050 (Kaza et al. in What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050, The World Bank Group, Washington, DC, USA, 2018). In many cities, growing waste is the primary driver of environmental pollution. Nationally, governments have initiated several programs to improve cleanliness by developing systems that alert businesses when it’s time to empty the bins. Current research proposes an enhanced, accurate, real-time object detection system to address the problem of trash accumulating around containers. This system involves numerous trash cans scattered across the city, each equipped with a low-cost device that measures the amount of trash inside. When a certain threshold is reached, the device sends a message with a unique identifier, prompting the appropriate authorities to take action. The system also triggers alerts if individuals throw trash bags outside the container or if the bin overflows, sending a message with a unique identifier to the authorities. Additionally, this paper addresses the need for efficient garbage classi...