Automated Waste Sorting System Using Computer Vision and Robotic Manipulation

Metropolitan areas and industrial zones across the globe are worried about rising solid waste production levels. There are three severe problems caused by poor waste segregation methods: landfills fill excessively, the environment becomes polluted and human health suffers. Standard waste management methods depend on physical staff activities while this practice restricts operational speed and creates unsafe working conditions for personnel. The authors developed an Automated garbage Sorting System using robotic manipulation and computer vision approaches to run intelligent real-time separation and classification of waste for resolving the mentioned issues. The proposed system operates through a YOLOv8 model built within Ultralytics YOLO. The system used its dedicated garbage photo database to teach the model to perform category assignments such as hazardous material and recyclable and biodegradable waste. Through the computer vision module the system successfully detects and sorts waste materials on conveyor elements with either real-time videos or static pictures as inputs. The robotic manipulator employs received location and classification data to sort waste materials physically into separate bins based on the detection categorization. The automated handling procedure simultaneously achieves higher sorting precision while requiring fewer human operators while keeping workers secure from potential health dangers. The YOLOv8 model provided outstanding performance capabilities with real-time detection functions while developers tested it in Python-based Colab notebooks. Testing showed that these systems function effectively in both city waste management infrastructures and industrial recycling units through their evaluation of accuracy rates and speed capabilities and system reliability.