A Review: One-Shot Object Detection Methods for Conditional Detection of Retail and Warehouse Products

Facilitating the rapid dispatch and replenishment of products is a critical task in most large warehouses. Automated systems often rely on Deep Learning based object detection methods to monitor operations. A major challenge is the significant requirement for annotated data, and the system’s difficulty in adapting to new products. In contrast, human operators can quickly learn to recognize and adapt to new products with just a single example. This survey focuses on methods that enable conditional detection using a single support example per class. We first introduce common feature fusion techniques and discuss datasets suitable for warehouse and retail products. Next, we provide a comprehensive overview of the current State-Of-The-Art in One-Shot Object Detection. We categorize these approaches based on their detectors, which identify the object’s bounding boxes and classes. Then, we delve into detailed implementations of these methods, analyzing how they leverage this innovative vision approach to improve performance. Finally, we identify promising current trends in this emerging field.