Bridging between Computer and Robot Vision through Data Augmentation: a Case Study on Object Recognition
This work addresses the problem of improving object recognition for robots in unconstrained settings, though it is incremental as it builds on existing convolutional architectures.
The paper tackles the performance gap in object recognition for robot vision compared to computer vision by introducing a data augmentation layer that simulates robot visual experiences, achieving up to a 7% increase in performance across three benchmark databases.
Despite the impressive progress brought by deep network in visual object recognition, robot vision is still far from being a solved problem. The most successful convolutional architectures are developed starting from ImageNet, a large scale collection of images of object categories downloaded from the Web. This kind of images is very different from the situated and embodied visual experience of robots deployed in unconstrained settings. To reduce the gap between these two visual experiences, this paper proposes a simple yet effective data augmentation layer that zooms on the object of interest and simulates the object detection outcome of a robot vision system. The layer, that can be used with any convolutional deep architecture, brings to an increase in object recognition performance of up to 7\%, in experiments performed over three different benchmark databases. Upon acceptance of the paper, our robot data augmentation layer will be made publicly available.