A fusion Siamese-ResNet network applied for low shot object detection in warehouses

Abstract

Warehouses are a storage areas with high flow of goods. As part of the robotization of these areas, one of the major problems, attracting the attention of researchers, is defined by the automation of dispatching and renewing tasks when new products arrive. Indeed, having few information, automatic detection of these new products requires an update of the intelligent system, i.e. a new training step and therefore a stop in the warehouse production line. According to the literature, a novel branch of computer vision that consists in identifying and locating objects in an image with few information is called Low Shot Object Detection (LSOD). Using this solution, neural networks can automatically find new products with no need of any additional training step. To do so, neural networks architectures have been evolved by merging extracted features. This article presents a novel method that consists in merging layers convolution Siamese-ResNet network to include new products.