Implementation of YOLOv11 on a Rotating Mechanical System for Smart Waste Sorting

Abstract

Waste management still faces limitations in efficiency and consistency in manual sorting. Hence, a computer vision-based smart waste-sorting system integrated with mechanical actuation is required. This study applies YOLOv11 for object detection and waste classification in a smart waste-sorting system integrated with a rotational mechanism that directs objects into organic and inorganic categories. The dataset comprised 17.231 waste images collected from multiple publicly available datasets on Kaggle, resized to 512 × 512 pixels, normalized, and augmented. The YOLOv11n model was trained for 100 epochs with a batch size of 16, an automatic optimizer configuration, and an initial learning rate of 0.01 to identify five classes: Organic, Paper, Glass, Plastic, and Metal. The system transmitted detection results from a laptop or mini PC to the ESP32 via UART to control the sorting mechanism using two servos. The five detection classes were mapped to two operational categories: organic and inorganic. Model evaluation resulted in an overall precision of 77.3%, recall of 67.9%, F1-score of 72.29%, mAP@50 of 78.2%, and mAP@50–95 of 59.1%. Physical testing yielded 178 successful trials out of 180 (98.89%). The average system response time was 1.78 ± 0.16 seconds, rotation movement time was 1.77 ± 0.03 seconds, and total processing time was 3.54 ± 0.17 seconds. The mechanism operated smoothly for objects weighing 2–159 grams during testing. The results indicate that integrating YOLOv11 with a rotational mechanism can support automated smart waste sorting under the tested conditions.