Small Object Detection in Industrial Recycling: A New Dataset and YOLO Performance Evaluation
This study addresses the challenge of detecting small, densely packed, and overlapping objects in industrial recycling scenarios by introducing the first dedicated small-object detection dataset, comprising over 10,000 images and 120,000 annotated instances. The authors systematically evaluate the performance of YOLO-family models across three tasks: small object detection, length measurement, and anomaly detection. To enhance robustness to scale variations, they propose an anomaly detection method leveraging high-resolution inputs, scale-robust data augmentation, and synthetic image generation. Experimental results demonstrate that the selected optimal YOLO variant achieves superior accuracy, efficiency, and stability, offering a reliable solution for automating industrial recycling processes and establishing a benchmark for future research in this domain.