| 方潜生,张亮,颜普,徐朝阳.基于改进LiteFlowNet3网络的轻量化光流估计方法[J].安徽建筑大学学报,2024,32(4):38-43 |
| 基于改进LiteFlowNet3网络的轻量化光流估计方法 |
| Dynamic Response of Fixed Supported Liquid Crystal Elastomer Beam under Moving Illumination |
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| DOI： |
| 中文关键词: 光流估计 轻量化 LiteFlowNet3 深度可分离卷积 |
| 英文关键词: optical flow estimation lightweight LiteFlowNet3 depth-separable convolution |
| 基金项目:安徽省高校自然科学重点项目（2022AH050249） |
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| 中文摘要: |
| 现有的光流估计方法参数量大，计算耗时，难以满足实时性需求。为此，提出一种基于改进LiteFlowNet3网络的轻量化光流估计方法。使用池化和深度可分离卷积替换 LiteFlowNet3网络中的常规卷积层，大幅减少网络模型参数量。在损失函数中增加了光流梯度的损失，强调训练中对光流边界的监督，在不增加参数的前提下提升性能。改进的 LiteFlowNet3网络参数量仅为 0.78 M。实验结果表明，改进的 LiteFlowNet3光流估计方法在 Sintel数据集的 Clean 和 Final 序列上的端点误差分别为 2.69 和 4.12，单次推理时间仅为 25 ms，性能优于其他的轻量级光流方法。 |
| 英文摘要: |
| Existing optical flow estimation methods have large parameter counts and time-consuming computations, which are difficult to meet the real-time demand. To solve this problem, a lightweight optical flow estimation method based on improved LiteFlowNet3 is proposed. Pooling and depth-separable convolution are used to replace the conventional convolutional layers in LiteFlowNet3, significantly reduce the number of network model parameters. The loss of the optical flow gradient is added to the loss function to emphasize the supervision of the optical flow boundaries during training, improved the performance without increasing the parameters. The improved LiteFlowNet3 parameter count is only 0.78 M. Experimental results show that the improved LiteFlowNet3 optical flow estimation method has end-point errors of 2.69 and 4.12 on Clean and Final sequences of the Sintel dataset. Respectively, the single inference time is only 25 ms. The performance outperforms that of other lightweight optical flow methods and is highly competitive. |
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