Lightweight gesture recognition algorithm for complex indoor environments
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.
万方数据
维普
