<scp>GF</scp> ‐ <scp>YOLOv8</scp> : A Lightweight Gesture Recognition Algorithm Based on Adaptive Feature Fusion Pyramid Network

As an intuitive form of interaction, gestures are widely used in fields like smart live streaming and smart homes, significantly improving user efficiency and the convenience of human‐computer interaction. However, existing gesture recognition methods often suffer from large model sizes and high computational complexity, making them unsuitable for real‐time deployment. To address these issues, we propose GF‐YOLOv8, a lightweight gesture recognition model based on YOLOv8 (where ‘GF’ stands for the initials of two optimization modules: G represents the GB‐C2f module, and F represents the FFPN network), designed to reduce parameters while maintaining detection accuracy. First, by designing the GB‐C2f module, which integrates the lightweight network GhostNet, the model significantly reduces computation and parameters with minimal loss in accuracy. Second, a fusion feature pyramid network (FFPN), based on ASFF and AFPN algorithms, and enhances the interaction between non‐adjacent layers to improve gesture feature perception. Compared to the original YOLOv8 model, the proposed method demonstrates significant improvements on both public and self‐constructed datasets: GFLOPs and parameter count are reduced by 46.9% and 44.9%, respectively, with the mAP value remaining almost unchanged. The improved model enables efficient and accurate gesture recognition on mobile and embedded devices, achieving a substantial reduction in model size without compromising accuracy. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.