HockeyAI: A Multi-Class Ice Hockey Dataset for Object Detection

The fast paced nature of ice hockey presents unique challenges for object detection, particularly in tracking the puck---a small, fast moving object that is critical to gameplay analysis. This paper introduces HockeyAI, a novel open source dataset specifically designed for multi-class object detection in ice hockey. The dataset includes 2,101 high resolution frames extracted from professional games in the Swedish Hockey League (SHL), annotated in the You Look Only Once (YOLO) format. Annotations span 7 classes, covering dynamic objects such as the players and the puck, as well as static rink elements such as goalposts and face-off circles. The dataset is derived from diverse SHL games across multiple seasons and teams, ensuring a rich variety of scenarios reflective of real world gameplay. A fine tuned YOLOv8 medium model is also provided, demonstrating high performance across all classes. Key comparisons highlight the dataset's advancements over existing resources, addressing their limitations such as incomplete class coverage, low resolution, and inconsistent annotations. The dataset, model, and an interactive demo are publicly available on Hugging Face under an open source license, fostering further collaboration in sports related computer vision applications (https://huggingface.co/SimulaMet-HOST/HockeyAI).