SiamYOLOv8: a rapid conditional detection framework for one-shot object detection

Abstract

Deep learning networks typically require vast amounts of labeled data for effective training. However, recent research has introduced a challenging task called One-Shot Object Detection, which addresses scenarios where certain classes are novel and unseen during training and represented by only a single labeled example. In this paper, we propose a novel One-Shot Object Detection model applicable to Conditional Detection without over-training on novel classes. Our approach leverages the strengths of YOLOv8 (You Only Look Once v8), a popular real-time object detector. Specifically, we incorporate a Siamese network and a matching module to enhance One-Shot Object Detection capabilities. Our proposed model, SiamYOLOv8, enables exploration of new applications without being limited by its training data. To evaluate the performance, we introduce a novel methodology for using the Retail Product Checkout (RPC) dataset “(https://github.com/MatD3mons/Conditional-Detection-datasets/tree/main/RPC)”, and extend our evaluation using the Grozi-3.2k dataset “(https://github.com/MatD3mons/Conditional-Detection-datasets/tree/main/GROZI-3.2k)”. In such contexts, new products often lack sufficient data for continuous Deep Learning methods, making individual case identification difficult. Our model outperforms SOTA models, achieving a significant performance improvement of 20.33% increase in Average Precision (+12.41 AP) on the Grozi-3.2k dataset and 25.68% increase (+17.37 AP) on the RPC dataset.