Exploring the Performance of YOLOv11: Detecting Compostable and Non-Compostable Kitchen Waste in Real-Time Applications

This paper investigates the advancements of YOLOv11, the latest model in the YOLO series in real-time object detection tasks on small datasets of compostable and non-compostable kitchen waste.Using a custom compostable and non-compostable kitchen waste dataset, YOLOv11 achieves an accuracy of 90.7% and a mean Average Precision (mAP) of 0.91, with a reduced inference time of 10.5 milliseconds.The study highlights YOLOv11's architectural enhancements, training methodology, and potential applications in waste management.While YOLOv11 sets a new benchmark in object detection, challenges like high computational demands, paving the way for future research on optimization for edge devices