Defect Detection of Micro-Precision Glass Insulated Terminals

Abstract

Micro-precision Glass Insulated Terminals (referred to as glass terminals) are the core components used in precision electronic equipment and are often used for electrical connections between modules. As a glass terminal, its quality has a great influence on the performance of precision electronic equipment. Due to the limitations of materials and production processes, some of the glass terminals produced have defects, such as missing blocks, pores and cracks. At present, most of the defect detection of glass terminals is done by manual inspection, and rapid detection easily causes eye fatigue, so it is difficult to ensure product quality and production efficiency. The traditional defect detection technology is difficult to effectively detect the very different defects of the glass terminal. Therefore, this paper proposes to use deep learning technology to detect missing blocks. First, preprocess the sample pictures of the missing block defects of the glass terminal, and then train the improved Faster Region-CNN deep learning network for defect detection. According to the test results, the accuracy of the algorithm in detecting missing defects in the glass terminal is as high as 93.52%.

Image Generation and Recognition for Railway Surface Defect Detection

“…The classic GAN model has evolved into various forms and architectures, such as DCGAN [ 13 , 14 , 15 , 16 ], Pix2Pix [ 17 , 18 , 19 ], CycleGAN [ 20 , 21 , 22 ], and StyleGAN [ 23 , 24 , 25 , 26 , 27 , 28 ]. There have been several attempts to generate surface defects using these models.…”

“…The generator takes a vector of input images and generates an image with an RGB color matrix, while the discriminator uses the information from the RGB images to output a scalar probability. Liu et al [ 15 ] applied DCGAN to generate defective images of micro-precision glass-encapsulated electrical connectors. In their study, both the generator and discriminator had five convolutional layers.…”