A Rapid and Efficient Method for Recognizing Basketball Umpire Signals Using ICCG-YOLO

In addressing the complex challenge of real-time and precise recognition of umpire signals in sporting events, we introduce ICCG-YOLO, a rapid and effective approach that builds upon the YOLO-v5 architecture. Our method innovatively incorporates Involution operations within the CSP components for superior spatial information modeling and channel-wise parameter sharing, significantly reducing parameter count while capturing extensive features through larger kernels. This stable approach between computational resource & performance and detection accuracy is further enhanced by integrating the CoordAttention block for precise localization and recognition of signals with minimal parameter addition. We also refine the model’s up-sampling process with the CARAFE (Content-Aware ReAssembly of FEatures) block, enabling content-driven feature enlargement that amplifies the receptive field without compromising the model’s compact stature. Complementing this, model compression is achieved using Ghost convolution, capitalizing on simple linear transformations for feature generation, paired with an improved activation function to activate all neural network neurons fully. This results in a multi-scale detection capability while maintaining a moderate depth and reducing overall model complexity. Our experiments on a custom dataset for umpire signal detection and the Chalearn dataset for general gesture recognition in diverse scenarios have successfully validated the high accuracy, rapid processing, and versatility of ICCG-YOLO.