基于深度学习的汽车轮毂缺陷自动分割技术

针对建立轮毂无损检测智能化平台的需要，本文提出一种基于深度学习算法的轮毂缺陷自动分割方法，利用卷积神经网络的结构和径向基函数神经网络的非线性特点，构造一种深度学习网络结构来模拟人类的视觉感知。本文依据汽车轮毂X射线图像，利用U-Net网络来训练轮毂缺陷分割模型，并在感兴趣区域的基础上模拟人脑层次感知系统，该层次感知系统能识别感兴趣区域的灰度像素，通过深度学习分层网络和卷积神经网络，逐层提取缺陷区域的内在特征，从而实现轮毂缺陷的自动分割。实验表明本方法针对复杂轮毂缺陷的识别率达到90%以上，且识别时间开销大约5ms/张，优于传统方法。可见该方法能够满足轮毂缺陷自动分割的需求，具有潜在的应用前景。

Automatic Segmentation Technology of Automobile Wheel Hub Defects Based on Deep Learning

In response to the need of establishing an intelligent platform for wheel non-destructive testing, this paper proposed an automatic wheel hub defect segmentation method based on deep learning algorithms. This method used the structure of convolutional neural network and the nonlinear characteristics of radial basis function neural networks to construct a deep learning network structure to simulate human visual perception. This paper made use of the U-Net network to train a wheel hub defect segmentation model based on X-ray images of the car wheel. And based on the region of interest, the human brain hierarchical perception system was simulated. The hierarchical perception system can identify the gray pixels in the region of interest. By means of deep learning layered network and convolutional neural network, it extracted the intrinsic features of the defect area layer by layer, so as to realize the automatic segmentation of wheel defects. The experimental results showed that the recognition rate of this method for complex wheel defects reached over 90%, and the recognition time overhead was about 5ms / sheet, which was better than the traditional method. It can be seen that this method could meet the need for automatic segmentation of wheel hub defects and has potential application prospects.