To address the low detection rates in gesture recognition algorithms caused by complex indoor environments, diverse hand appearances,and variable recognition angles,and to facilitate deployment on mobile devices,we propose a novel SA-YOLOv8 gesture recognition algorithm.Initially,an improved CB-ShuffleNetV2 lightweight network is utilized as the backbone for extracting gesture features,ensuring accuracy while reducing model parameters and computational load,facilitating real-time recognition on smart home devices.Subsequently,an asymptotic feature pyramid network(AFPN)is integrated into the Neck layer for multi-scale feature fusion of gesture information, employing adaptive spatial fusion operations to mitigate interference from complex factors and preserve detailed hand information,thereby enhancing the model's robustness.Finally,the Shape-IoU loss function is introduced during the loss calculation phase,increasing the model's sensitivity and accuracy for irregular and small-scale gestures at a distance. The experiments demonstrate that SA-YOLOv8 achieves an average detection precision mAP@0.5 of 99.80%on the ASL-6 dataset and 99.83%on the full ASL dataset,marking a 4.47%and 4.5%improvement over the original YOLOv8 model,along with an 80.18%reduction in parameter volume and a 77.46%decrease in computational demand.The improved algorithm shows a significant enhancement in gesture recognition performance and is more lightweight,making it suitable for deployment on mobile devices.