# Bridging between Computer and Robot Vision through Data Augmentation: a Case Study on Object Recognition

Source: [Bridging between Computer and Robot Vision through Data Augmentation: a Case Study on Object Recognition](<https://www.sota2.com/research/paper/68fa63501e209131ce9fe0cc>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:38:15.653Z

Authors: Antonio D'Innocente, Fabio Maria Carlucci, Mirco Colosi, Barbara Caputo

Published: 2017-05-05T09:05:11.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1705.02139>)

[PDF](<https://arxiv.org/pdf/1705.02139.pdf>)