The National Key Technologies R&D Program of China
针对U-Net存在的小目标分割精度低、计算复杂度高、分割速度慢等问题，构建了基于空洞卷积和重构采样单元的U-Net网络（U-Net network based on dilated convolution and reconstructed sampling units，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，设计了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，构建了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，从而增强了神经网络的特征提取能力以及降低了计算成本。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和R2U-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，结果表明所提方法能够更加快速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。
Aiming at the problems of low segmentation accuracy of small targets, high computational complexity, and slow segmentation speed in U-Net, a new U-Net network based on dilated convolution and reconstructed sampling units (DSU-Net) is constructed. In 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 that combine pooling and convolution are constructed, and depthwise separable convolution is used for feature extraction, thereby enhancing the feature extraction capability of 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 U-Net, ResU-Net and R2U-Net on the three metrics of IoU, Dice Coeff and F1 Score. Finally, 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 quickly and accurately, so as to solve the problem of efficiently and accurately detecting gear failure in the gear contact fatigue test.