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

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 classification while reducing computing costs to improve resource utilization. Two-stage lightweight deep learning models based on YOLOv5 and YOLOv8 are adopted to significantly decrease the number of parameters and processes, thereby reducing hardware requirements. In this study, trash is first classified into primary categories, which are further subdivided. The primary categories include full trash containers, trash bags, trash outside containers, and wet trash containers. YOLOv5 is particularly effective for classifying small objects, achieving high accuracy in identifying and categorizing different types of waste products on hardware without GPU capabilities. Each main class is further subdivided using YOLOv8 to facilitate recycling. A comparative study of YOLOv8, YOLOv5, and EfficientNet models on public and newly constructed garbage datasets shows that YOLOv8 and YOLOv5 have good accuracy for most classes, with the full-trash bin class achieving the highest accuracy and the wet trash container class the lowest compared to the EfficientNet model. The results demonstrate that the system effectively addresses the reliability issues of previously proposed systems, including detecting whether a trash bin is full, identifying trash outside the bin, and ensuring proper communication with authorities for necessary actions. Further research is recommended to enhance garbage management and collection, considering target occlusion, CPU and GPU hardware optimization, and robotic integration with the proposed system.