基于改进U-Net网络的齿轮点蚀测量

针对U-Net存在的小目标分割精度低、计算复杂度高、收敛慢的问题,构建基于空洞卷积和重构采样单元的U-Net网络(DSU-Net).在DSU-Net中,为增大图像特征提取的感受野并融合多尺度信息,设计具有不同膨胀率的空洞卷积层;针对池化过程丢失大量语义信息的缺点,构建将池化与卷积相结合的采样单元,并运用深度可分离卷积进行特征提取,从而增强神经网络的特征提取能力并降低计算成本.两个公开医学图像数据集的实验结果表明,在IoU、Dice Coeff和F1 Score三个评价指标上,DSU-Net较U-Net、ResU-Net、R2U-Net和U-Net++有着更好的分割性能.最后,将DSU-Net应用于齿轮点蚀的视觉测量,结果表明所提出方法能够更加精确地计算出齿轮点蚀面积率,从而解决了齿轮接触疲劳试验中高效准确检测齿轮失效的难题.

Gear pitting measurement based on improved U-Net network

Aiming at the problems of low segmentation accuracy of small targets, high computational complexity, and slow convergence in the U-Net, an U-Net network based on dilated convolution and reconstructed sampling units (DSU-Net) is constructed. In the DSU-Net, in order to increase the receptive field of image feature extraction and fuse multi-scale information, dilated convolutional layers with different dilation rates are designed; in view of the shortcoming of losing a large amount of semantic information during the pooling process, sampling units which combine pooling and convolution are constructed, and depthwise separable convolution is used for feature extraction, thereby enhancing the feature extraction capability of the neural network and reducing the computational cost. The experimental results of two public medical image datasets show that DSU-Net has better segmentation performance than the U-Net, the ResU-Net, the R2U-Net and the U-Net++ on the three metrics of IoU, Dice Coeff and F1 Score. Finally, the DSU-Net is applied to the visual measurement of gear pitting. The results show that the proposed method can calculate the gear pitting area ratio more accurately, so as to solve the problem of efficiently and accurately detecting gear failure in the gear contact fatigue test.