Recycling System and method for Manual Smashing of Containers with Computer Vision-Based Operation Monitoring and User Crediting

Abstract

A computer vision-enabled recycling system facilitates the disposal of one or more containers of any recyclable material through a manual compression mechanism integrated with a recycling receptacle featuring a designated insertion aperture. A camera processor, interfaced with a cloud- based computational unit, captures image sequences of the compression process. A computer vision algorithm processes these sequences to detect the state transitions of the compression mechanism, from an open to a closed position, isolates relevant image frames, and classifies the compressed object as an eligible container, a non-eligible container, or background using machine learning-based image recognition techniques. The system further verifies the insertion of the classified object into the receptacle. A data aggregation processor logs validated recycling events and assigns user-specificcredits accordingly. By integrating image-based classification, mechanical processing, and cloud- driven analytics, the system enhances recycling efficiency, object classification accuracy, and user participation tracking within waste management infrastructures.

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