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

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PCB Assurance currently relies on manual physical inspection, which is time consuming, expensive and prone to error. In this study, we propose a novel automated segmentation algorithm to detect and isolate PCB components from the boards called EC-Seg. Segmentation and component localization is a vital preprocessing step in component identification, component authentication, as well as in detecting logos and text markings in components. EC-Seg is an efficient method to automate Quality assurance tool-chains and also to aid Bill of Material Extraction in PCBs. Finally, EC-Seg can be used as a Region proposal algorithm for object detection networks to detect and classify microelectronic components, and also to perform sensor fusion with X-Rays to aid in artifact removal in PCB X-Ray tomography. Index Terms—PCB Hardware Assurance, Component Segmentation, Component detection, AutoBoM, Physical Inspection, Visual inspection, Counterfeit detection

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