ROBO: Robust, Fully Neural Object Detection for Robot Soccer

TL;DR

This paper introduces ROBO, an efficient neural network architecture tailored for object detection in robot soccer, significantly reducing computation time while maintaining high accuracy compared to existing models.

Contribution

The paper presents a novel neural network design optimized for robot soccer environments, achieving 35x faster inference than Tiny YOLO with improved average precision.

Findings

35 times faster inference than Tiny YOLO

Superior average precision in object detection

Slightly reduced localization accuracy

Abstract

Deep Learning has become exceptionally popular in the last few years due to its success in computer vision and other fields of AI. However, deep neural networks are computationally expensive, which limits their application in low power embedded systems, such as mobile robots. In this paper, an efficient neural network architecture is proposed for the problem of detecting relevant objects in robot soccer environments. The ROBO model's increase in efficiency is achieved by exploiting the peculiarities of the environment. Compared to the state-of-the-art Tiny YOLO model, the proposed network provides approximately 35 times decrease in run time, while achieving superior average precision, although at the cost of slightly worse localization accuracy.