Tiny electronic component detection based on deep learning

Abstract

Tiny electronic component detection is mostly suffered from compact size and multiple interferences of environment. To solve this problem, this paper presents an object detection method based on convolutional neural networks (CNNs). This method combines residual blocks in residual networks with CNNs. The CNNs can reduce the dimensions of the image, and the residual block in the residual network can train deeper neural networks, and then use the successful application of CNNs in computer vision to improve the recognition rate of components and improve the efficiency of automated processing of tiny electronic components. After the hyper-parameter adjustment, this method achieved 95.63% accuracy on our test set.Tiny electronic component detection is mostly suffered from compact size and multiple interferences of environment. To solve this problem, this paper presents an object detection method based on convolutional neural networks (CNNs). This method combines residual blocks in residual networks with CNNs. The CNNs can reduce the dimensions of the image, and the residual block in the residual network can train deeper neural networks, and then use the successful application of CNNs in computer vision to improve the recognition rate of components and improve the efficiency of automated processing of tiny electronic components. After the hyper-parameter adjustment, this method achieved 95.63% accuracy on our test set.