Affiliation:
1. University of Florida, Gainesville, Florida, USA
Abstract
Abstract
This paper evaluates several approaches for automating the identification and classification of logos on printed circuit boards (PCBs) and ICs. It assesses machine learning and computer vision techniques as well as neural network algorithms. It explains how the authors created a representative dataset for machine learning by collecting variants of logos from PCBs and by applying data augmentation techniques. Besides addressing the challenges of image classification, the paper presents the results of experiments using Random Forest classifiers, Bag of Visual Words (BoVW) based on SIFT and ORB Fully Connected Neural Networks (FCN), and Convolutional Neural Network (CNN) architectures. It also discusses edge cases where the algorithms are prone to fail and where potential opportunities exist for future work in PCB logo identification, component authentication, and counterfeit detection. The code for the algorithms along with the dataset incorporating 18 classes of logos and more than 14,000 images is available at this link: https://www.trusthub.org/#/data.
Cited by
3 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
1. DeepICLogo: A Novel Benchmark Dataset for Deep Learning-Based IC Logo Detection;2023 IEEE Physical Assurance and Inspection of Electronics (PAINE);2023-10-24
2. Deep Learning for Logo Detection: A Survey;ACM Transactions on Multimedia Computing, Communications, and Applications;2023-10-23
3. An End-to-End Marking Recognition System for PCB Optical Inspection;2022 IEEE Physical Assurance and Inspection of Electronics (PAINE);2022-10-25