# An Intelligent Vision System for Detecting Defects in Micro-Armatures for Smartphones
## 5 Conclusions  
In industrial applications, workpiece images in many cases are similar to our dataset due to automation and standardization. ANNs and complex algorithms are in use today in artificial vision, and the use is in some cases very useful. For example, some defects of a sample can be easily detected by classical computer vision technology, while the combination of deep learning techniques, and traditional computer vision techniques should be considered. Our experimental results provide a reference and demonstration for the cooperation of DCNNs and complex algorithms.  
In this study, a system based on classical computer vision and deep learning was proposed to detect the micro-armature defects. In our dataset, the rough location of defects is relatively stable,  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Predicted Positive** & **Predicted Negative** \\ \hline
**True Positive** & 2785 (\(TP\)) & 207 (\(FN\)) \\
**True Negative** & 187 (\(FP\)) & 1809 (\(TN\)) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Confusion matrix.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Accuracy (\%)** \\ \hline VGG-16 & 78.4 \\ AlexNet & 78.2 \\ ResNet-50 & 78.3 \\ VGG-16+B345 & 81.2 \\ FM & 95.8 \\ VGG-16-ROC & 93.6 \\ VGG-16+FM & 92.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The detection results of our model and the comparison to only DCNNs. “B345”represents the combination of “conv3_3”, “conv4_3”, and “conv5_3”, “\(FM\)” represents our front-end module, “VGG-16-ROC” means that the neural network input is only ROC.  
and partial defect detection can be easily achieved by classical computer vision technology. In order to improve the accuracy of our system, for the defective parts that are easy to be identified, we will achieve the defection work by classical computer vision technology, and then only feed the complex parts into the neural network if needed. This is similar to L1 regularization, which can reduce part parameters of the network to 0 and achieve the effect of parameter sparsity, for which we use the front-end module to make the neural network have similar ability. We conducted many experiments, the results of which proved that our method was superior in accuracy, and met the requirements of industrial manufacturing.  
In general, the main contributions of this work are as follows:  
1. According to the workpiece, the supporting fixture, optical positioning system, and lighting system are designed, and the defect detection algorithm matching our hardware is designed.
2. Through experiments, it is proven that the combination of DCNNs and complex algorithms is very useful in some cases, providing a reference and demonstration for the application of computer vision technology in industrial detection.
3. In our work, we combine traditional computer vision technology and DCNNs to achieve the task, so as to improve the detection accuracy. This two-stage idea could also be considered for use in deep learning techniques, i.e., a two-stage approach similar to Faster R-CNN [29]. This two-stage idea is worth considering as we focus more on accuracy.  
The difficult in our experiments is that some of the ROCs are very prone to controversy in classification. More data processing methods will be explored to solve this problem. Note that we are not talking about data augmentation. In the future, we will continue to optimize the front-end module and network structure to improve the accuracy.  
The authors [PERSON] and [PERSON] contributed equally to this work; project administration, J.W.; resources, X.F. and S.H.  
This presented work was supported by Sichuan Science and Technology Program (No. 2019 YFG0359).  
The authors declare no conflict of interest.