Counter-Strike Character Object Detection via Dataset Generation

This paper addresses the challenge of developing robust object detection systems in the context of Valve’s Counter-Strike by in-troducing a novel, high-quality dataset generated using a complex image generator built within the Unity game engine. This generator mimics the original game’s environment and character interactions, capturing the complexity of in-game scenarios. The dataset pro-vides a valuable resource for training models like the YOLOv9 algorithm, which we employ to develop an object detection system that achieves high precision and recall, in turn proving the usability of our dataset. Our dataset and demonstrated model could be used for object detection in future multi-modal autonomous agents, like the one we propose at the end of the paper.