# Customized Convolutional Neural Networks Technology for Machined Product Inspection
## 1 Introduction  
Over the past few decades, the application of computer-aided design and analysis had gradually increased in the engineering industry for applications such as signal processing or simulation testing. In the past, manual inspection not only required a lot of labor, the test results may also be inaccurate, affecting the quality of the product, due to human factors, such as fatigue and manual measurement errors. Therefore, automatic optical inspection is beginning to be used more and more; nowadays, the technological advancement of hardware and software provides space for research using artificial intelligence [1], image processing [2], computer vision [3], and machine learning [4], which are the main research areas of artificial intelligence. In particular, image processing combined with deep learning detection is becoming more and more popular, combining the deep learning model with the existing optical inspection system; it was a technological breakthrough that could alleviatethe bottleneck of the manufacturing defect detection system, and also achieved the goal of advancing the manufacturing process. Image classification technology in deep learning provides a new solution for image detection and can improve the accuracy of image detection [5; 6]; in addition, with the advancement of graphics processing units, the computing power of the hardware has been greatly improved. The well-known deep learning frameworks, such as TensorFlow [7] and PyTorch [8], have also been developed, promoting the advancement of technology in the field of deep learning. A well-known YOLO [9] is widely used for the purposes of object detection and image processing. Many studies have used convolutional neural network models to classify different images [10]; based on the deep learning structure, convolutional neural network models are easy to train and can automatically search for useful features [11; 12]. Thus, deep learning technology can be deployed to inspect and determine various image defects, and has been proven to be a very effective method [13]. At present, well-known convolutional neural networks include LeNet [14], VGG [15], DarkNet-19 [16], and the deep residual networks ResNet-34 [17] and DarkNet-53 [13]. The model structure of DarkNet-19 is similar to that of VGG [15]. DarkNet-19 has 19 convolutional layers and 5 maximum pooling layers. DarkNet-53 combines the elements of DarkNet-19 and ResNet [17]. For massive data, DarkNet-53 is much more effective than DarkNet-19 [18]. At the same time, DarkNet-53 achieves the highest floating point calculation speed per second in the network structure. This means that its network structure can make full use of the GPU [13]. Nevertheless, the deeper the network, the more difficult it is to converge. Many researchers have changed the Activation function [19] for preventing the gradient from disappearing. However, the problem still exists [20; 21]. The disappearance or explosion of the gradient may be due to the high nonlinearity of the deep network. DarkNet-53, ResNet-101, and ResNet-152 use residual learning methods to solve the problem of accuracy that increases first and then saturates. However, this will also lead to network redundancy [22]. Therefore, how to reduce the number of hidden layers and retain the efficient feature extraction via the characteristics of manipulating convolution is attractive in our study. One of the current studies on deep learning and optical inspection of the metal surfaces was by [PERSON] et al. [23]. They combined machine vision and deep learning to detect defects on the surface of metal cylinders and used a highly reflective metal surface as the test data. To improve the problem of high reflection of the metal's surface, they chose a strip light source and a round tube homogenizing plate as the light source setting. An all-white inner wall of a round tube was used. Since the strip light source can be placed in a round tube, the light source can reflect light uniformly in the inner wall of the round tube.