基于残差网络的河流表面时空图像测速法

针对在耀光、紊流、降雨等复杂含噪场景下时空图像中有效纹理特征变得模糊,使得现有纹理主方向检测算法精度受限的问题,结合深度学习的思想,提出了一种基于残差网络回归模型的时空图像测速(ResNet50-STIV)法,并借助残差网络回归模型强大的非线性学习能力构建了回归预测函数。通过构建人工合成数据集和包含复杂场景时空图像的天然河流数据集对残差网络回归模型进行试验,结果表明：提出的残差网络回归模型在人工合成数据集下的检测精测可达到0.1°,对于天然河流数据集,具有残差结构的ResNet回归模型的检测精度优于VGG16;从模型层数看,基于ResNet50的回归模型能较好地平衡检测精度以及执行效率,在正常场景下的检测精度达到0.7°,而在耀光、紊流、降雨场景下能控制在1.3°以内,ResNet50-STIV优于现有的时空图像测速法;与流速仪法在多场景下表面流速比测的最大相对误差小于12%。

Surface space-time image velocimetry of river based on residual network

Aiming at the problem that the effective texture features in the space-time image become blurred in complex noisy scenes such as flare, turbulence and rainfall, which limits the accuracy of the existing main direction detection algorithms of texture, a space-time image velocimetry method based on residual network regression model is proposed by combining the idea of deep learning, and the powerful nonlinear learning ability of the model is used to construct a regression prediction function. The residual network regression model is tested by constructing synthetic datasets and natural river datasets containing space-time images of complex scenes. The experimental results show that the detection accuracy of the proposed model under the synthetic datasets can reach 0.1°. For the natural river datasets, the detection accuracy of the ResNet regression model with residual structure is better than that of VGG16; in terms of the number of model layers, ResNet50-STIV can better balance the detection accuracy and execution efficiency. The detection accuracy in normal scenarios can reach 0.7°, while in flare, turbulence, and rainfall scenarios, it can be controlled within 1.3°, and ResNet50-STIV is better than the existing STIV method. Meanwhile, the maximum relative error is less than that of the instrument method in the surface velocity ratio measurement of multiple scenarios by 12%.