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Real-time monitoring system of crushed waste plastic particles using deep learning based object detection for particle size distribution monitoring
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Researchers built an AI-powered camera system that watches shredded plastic waste in real time and instantly measures the size of the particles as they're being recycled. This matters because when plastic pieces are sorted more precisely by size, recycling plants can separate and process materials more efficiently, using less energy and potentially reducing the amount of plastic fragments and microplastics that escape into the environment during recycling. While this study focused on improving the recycling process itself rather than testing health outcomes, better-controlled plastic breakdown is a step toward cleaner recycling systems overall.
This study developed a real-time monitoring system for the particle size distribution (PSD) of crushed waste plastics generated during the comminution stage of recycling. A deep learning-based object detection model was trained using four major post-consumer plastics (PET, PP, PE, and PS), achieving a mean Average Precision at an IoU threshold of 0.50 (mAP50) of 0.86, along with a recall of 0.83 and a precision of 0.99. A region of interest (ROI)-based object tracking pipeline with an additional false-positive class was implemented, resulting in an F1 score above 0.95. Among the evaluated size estimation approaches, the minAreaRect-based M3 method achieved < 4% error in D10, D50, and D90 estimation at 53 frames per second, enabling accurate real-time PSD analysis. Based on this model, feedback-guided monitoring framework was proposed to support dynamic adjustment of operating parameters when D50 deviated from a predefined range. This approach improves PSD uniformity, enhances downstream separation efficiency, and reduces energy consumption, demonstrating strong potential for application to diverse material types, shapes, and large-scale resource recovery processes.
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