# An Intelligent Vision System for Detecting Defects in Micro-Armatures for Smartphones
## 1 Introduction  
With the rapid expansion of network applications, computer vision technology has been successfully applied to the quality inspection of industrial production [1; 2; 3; 4; 5; 6], including glass products [1], fabrics [2; 3], steel surfaces [4], bearing rollers [5], and casting surfaces [6]. The inspection of these mentioned examples needs a matching algorithm to extract image features based on the actual defect situation. For glass products for packaging and domestic use, an intelligent system based on the classical computer vision technology was proposed for the automatic inspection of two types of defects [1]. For fabric quality control, an unsupervised learning-based automated approach to verify and localize fabric defects was proposed [2]. This approach was realized by using a convolutional denoising autoencoder network at multiple Gaussian pyramid levels to reconstruct image patches and synthesize detection results from the corresponding resolution channels. A two-fold procedure was proposed to extract powerful features [3]. First, the sample class was determined based on its background texture information, then the image was divided into 49 blocks to figure out which images contain defective regions. For the defects of steel surfaces, an inspection system with a dual lighting structure was proposed to distinguish uneven defects and color changes by surface noise [4]. In a previous study [5], a multi-task convolutional neural network applied to recognize defects was raised. Although there are many detection systems based on computer vision technology to solve product defects, few studies have focused on the defect inspection of micro-armatures. For the inspection of surface aluminum, a vision based approach and neural network techniques in surface defects inspection and categorization are proposed. The new vision inspection system, image processing algorithm, and learning system based on artificial neural networks (ANNs) were successfully implemented to inspect surface aluminum die casting defects [6]. Recently, deep convolutional neural networks (DCNNs) have been proved to be important methods in visual detection. However, some classical methods should still be considered. Literature [6] mentioned above is an example. For instance, in order to get better parameters, a suitable smart manufacturing strategy for real industrial conditions was proposed. The results of this dataset showed that the Adaboost ensembles provided the highest accuracy and were more easily optimized than ANNs [7]. Obviously, for now, there are some limitations with using classical methods alone or using DCNNs directly. For classical methods, for example, they usually come with high complexity of programming and less tolerance to data variability. With regard to the DCNNs, a large number of samples are needed for network training and the iterative optimization of parameters is partly a black-box operation [8]. In the industrial applications, part defects in the samples can be easily identified with a picture by the classical computer vision method. Therefore, we do not need to feed the whole picture into the network for discrimination, which can reduce the number of features to be identified. As a result, the network is easier to converge. Therefore, by combining the classical image recognition method and DCNNs, the landing speed of deep learning technology in industrial applications can be effectively improved. In this work, an intelligent detection system that combined the classical computer vision method and DCNNs was designed to automatically detect the quality defects of micro-motor armatures. Firstly, the quality, excluding the region of copper wire crossing (ROC), is decided based on the classical computer vision technology. If the first result is positive, the ROC will be extracted and sent to the DCNNs for identification. If the result is still positive, the image is a defect-free sample, otherwise the whole image is labeled defective.