Automated Waste Segregation to Optimize Recycling Efficiency using YOLOv11 Approach

Waste segregation plays a very important role in promoting efficient recycling of substances and thereby reducing pollution. Traditional methods of waste segregation can sometimes lead to human error which increases the need for optimized solutions. We proposed a system which will separate different types of waste using YOLOv11 (You Only Look Once) object detection algorithm. Additionally, the system is developed to be versatile as it detects the different types of waste such as ‘recyclable waste’, ‘kitchen waste’, ‘hazardous waste’ and ‘other waste’. Notably, the model achieved an overall mAP (mean Average Precision) of 93.9% for YOLOv11s and mAP of 93.8% for YOLOv11m model. The present system contributes to a cleaner and more sustainable environment by classifying the waste more accurately and efficiently. By automating the waste segregation using YOLOv11, the system also reduces the risks of exposure to hazardous materials.