Logo Classification and Data Augmentation Techniques for PCB Assurance and Counterfeit Detection

Abstract

In this manuscript, we present our work on Logo classification in PCBs for Hardware assurance purposes. Identifying and classifying logos have important uses for text detection, component authentication and counterfeit detection. Since PCB assurance faces the lack of a representative dataset for classification and detection tasks, we collect different variants of logos from PCBs and present data augmentation techniques to create the necessary data to perform machine learning. In addition to exploring the challenges for image classification tasks in PCBs, we present experiments using Random Forest classifiers, Bag of Visual Words (BoVW) using SIFT and ORB Fully Connected Neural Networks (FCN) and Convolutional Neural Network (CNN) architectures. We present results and also a discussion on the edge cases where our algorithms fail including the potential for future work in PCB logo detection. The code for the algorithms along with the dataset that includes 18 classes of logos with 14000+ images is provided at this link: https://www.trusthub.org/#/data Index Terms—AutoBoM, Logo classification, Data augmentation, Bill of materials, PCB Assurance, Hardware Assurance, Counterfeit avoidance