Dataset and Benchmarking of Real-Time Embedded Object Detection for RoboCup SSL
This provides a benchmark for real-time object detection in RoboCup SSL, addressing a specific gap for robotics researchers, but it is incremental as it applies existing methods to new data.
The paper tackles the lack of a dataset for object detection in the RoboCup Small Size League by presenting an open-source dataset and a pipeline for training and deploying models on low-power embedded systems, achieving 44.88% AP at 94 FPS with MobileNet SSD v1.
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.