复杂室内环境下轻量级手势识别算法
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西安工程大学

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TP391.41

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陕西省重点研发计划资助项目(2022GY-074),陕西省重点研发计划资助项目(2022GY-058)


Lightweight Gesture Recognition Algorithm for Complex Indoor Environments
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    摘要:

    针对室内环境背景复杂、手部多样、识别角度多变等因素导致手势识别算法检测率低,算法复杂难以在移动端设备部署,提出了一种SA-YOLOv8手势识别算法。首先,利用改进后的CB-ShuffleNet V2轻量级网络作为主干网络提取手势特征,在保证准确率的同时降低模型参数与计算量,方便模型部署在智能家居设备,保证识别的实时性。其次,在Neck层引入渐进特征金字塔网络AFPN实现手势信息的多尺度特征融合,通过自适应空间融合操作避免复杂因素干扰,保留手部细节信息,提高模型鲁棒性。最后,在损失函数阶段引入Shape-IOU损失函数,增加模型对非规则手势与远距离小尺度手势识别的敏感力与准确性。实验结果表明,SA-YOLOv8在ASL数据集上平均检测精度mAP50达到99.80%,相较于原始YOLOv8模型提高了4.47%,参数量下降80.18%,计算量减少77.46%。改进后的算法在手势识别方面效果提升明显,且模型更加轻量,适合部署在移动端设备中。

    Abstract:

    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-ShuffleNet V2 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 mAP50 of 99.8% on the ASL dataset, marking a 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.

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  • 收稿日期:2024-10-21
  • 最后修改日期:2024-11-18
  • 录用日期:2024-11-19
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