Abstract
The quantity of waste generated is increasing daily, demanding an intelligent waste management system for effective and timely solutions. Pollution induced by garbage has been one of the major problems globally for a long time. The unawareness among people about waste disposal causes a threat to living things and creates a major risk for sanitation workers during waste collection, sorting, and recycling. A method is proposed for real-time automatic waste material detection and segregation for easier recycling. The proposed approach uses the State-of-art deep learning architecture Mask RCNN to locate and classify waste objects from the natural environment. Further, the geometric features like centroid, orientation, and the clamping points of the objects are extracted to aid the robotic arm in grasping the waste object. Methods are investigated for ordering the objects in the scene for the energy-efficient automated system. The technique can be adapted into robotic machines for waste management to sort the waste on the road pavements and streets, providing safety, productivity, and reducing the risk on the sanitation workers.
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