Affiliation:
1. School of Electronic Information and Electrical Engineering, Huizhou University, Huizhou 516007, Guangdong, China
2. Guangdong Provincial Key Laboratory of Electronic Functional Materials and Devices, Huizhou University, Huizhou 516001, Guangdong, China
Abstract
Automatic recognition and positioning of electronic components on PCBs can enhance quality inspection efficiency for electronic products during manufacturing. Efficient PCB inspection requires identification and classification of PCB components as well as defects for better quality assurance. The small size of the electronic component and PCB defect targets means that there are fewer feature areas for the neural network to detect, and the complex grain backgrounds of both datasets can cause significant interference, making the target detection task challenging. Meanwhile, the detection performance of deep learning models is significantly impacted due to the lack of samples. In this paper, we propose conditional TransGAN (cTransGAN), a generative model for data augmentation, which enhances the quantity and diversity of the original training set and further improves the accuracy of PCB electronic component recognition. The design of cTransGAN brings together the merits of both conditional GAN and TransGAN, allowing a trained model to generate high-quality synthetic images conditioned on the class embeddings. To validate the proposed method, we conduct extensive experiments on two datasets, including a self-developed dataset for PCB component detection and an existing dataset for PCB defect detection. Also, we have evaluated three existing object detection algorithms, including Faster R-CNN ResNet101, YOLO V3 DarkNet-53, and SCNet ResNet101, and each is validated under four experimental settings to form an ablation study. Results demonstrate that the proposed cTransGAN can effectively enhance the quality and diversity of the training set, leading to superior performance on both tasks. We have open-sourced the project to facilitate further studies.
Funder
Guangdong Special Projects in Key Areas for Colleges
Subject
General Mathematics,General Medicine,General Neuroscience,General Computer Science
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献