Computer Vision-Based Plastic Waste Detection and Weight Estimation
Abstract
Plastic waste management is an increasingly critical environmental challenge due to the growing volume of waste and the limitations of conventional manual weighing methods, which are inefficient and prone to human error. This study proposes a computer vision-based system for automatic detection, classification, and weight estimation of plastic waste using the YOLOv8n model for object detection and Random Forest Regression for weight estimation. The YOLOv8n model is used to detect and classify seven types of plastic waste based on the Resin Identification Code (RIC), namely PET, HDPE, PVC, LDPE, PP, PS, and OTHER. Subsequently, weight estimation is performed using a Random Forest Regression model based on bounding box features, including width, height, area, aspect ratio, and perimeter. The proposed system is evaluated using an unseen test set to ensure unbiased performance measurement. Experimental results show that the YOLOv8n model achieves a mean Average Precision (mAP@0.5) of 91.93% and mAP@0.5:0.95 of 73.27%, while the Random Forest Regression model achieves an R² score of 95.5% with a Mean Absolute Error (MAE) of 4.28 grams. These results demonstrate that the integration of object detection and regression enables accurate and automatic estimation of plastic waste weight, thereby improving the efficiency and objectivity of waste management systems.
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