# Dataset and Benchmarking of Real-Time Embedded Object Detection for RoboCup SSL

Source: [Dataset and Benchmarking of Real-Time Embedded Object Detection for RoboCup SSL](<https://www.sota2.com/research/paper/68fa6a421e209131cea270f8>)

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

Data updated: 2025-10-25T23:20:37.043Z

Authors: Roberto Fernandes, Walber M. Rodrigues, Edna Barros

Published: 2021-06-28T12:09:36.000Z

## Abstract

When producing a model to object detection in a specific context, the first obstacle is to have a dataset labeling the desired classes. In RoboCup, some leagues already have more than one dataset to train and evaluate a model. However, in the Small Size League (SSL), there is not such dataset available yet. This paper presents an open-source dataset to be used as a benchmark for real-time object detection in SSL. This work also presented a pipeline to train, deploy, and evaluate Convolutional Neural Networks (CNNs) models in a low-power embedded system. This pipeline was used to evaluate the proposed dataset with state-of-art optimized models. In this dataset, the MobileNet SSD v1 achieves 44.88% AP (68.81% AP50) at 94 Frames Per Second (FPS) while running on an SSL robot.

## 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/2106.14597>)

[PDF](<https://arxiv.org/pdf/2106.14597.pdf>)