Neurons Perception Dataset for RoboMaster AI Challenge

Abstract

From virtual game to physical robot, games have witnessed the development of artificial intelligence (AI) technology, especially the data-driven technology represented by deep learning. Compared with virtual games, a physical robot game such as RoboMaster AI challenge needs to build a complete closed-loop architecture composed of perception, planning, control, and decision-making to support autonomous confrontation. Perception, as the eye of the robot, its performance in the complex environment depends on a massive dataset. Although there are many open perception datasets, these datasets are difficult to meet the needs of RoboMaster AI challenge due to the high dynamics of the task, the distinctiveness of the objects, and limited computing resources. In this paper, we release a dataset named Neurons11Neurons is a team dedicated to promoting the development of robot with deep neural network. We will release the code and dataset at https://github.com/DRL-CASIA/NeuronsDataset. perception dataset for RoboMaster AI challenge, which covers 3 tasks including monocular depth estimation, lightweight object detection, and multi-view 3D object detection, and makes up the data blank in this field. In addition, we also evaluate State-Of-The-Art (SOTA) methods on each task, hoping to provide an impartial benchmark for the development of perception algorithm.