Empirical Study One-stage Object Detection methods for RoboCup Small Size League

Abstract

Small Size League (SSL) is a division of the traditional RoboCup, founded to promote research in robots and AI. A fast and accurate real-time object detection model is essential for RoboCup SSL soccer robots, serving the design and development of competitive strategies. Specific state-of-the-art object detection methods have reported inference speed up to 94 FPS on the SSL open-source benchmark dataset, but only at intermediate accuracy. Considering the advancement in deep learning methods for feature extraction and object detection, in this paper, we conducted surveys and experiments on one-stage object detection methods provided in the MMDetection framework on the dataset for RoboCup SSL. YOLOX-tiny model achieved 58.60% AP, which is significantly higher than baseline methods, while maintaining an acceptable inference speed of 37 Frames Per Second (FPS). Other state-of-the-art one-stage methods have achieved very high performance, up to 74,10% Average Precision (AP). However, certain methods did not meet the minimum inference speed requirement of real-time object detection.