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@article{174666,
        author = {Snehal Ghogare and Dr Rashmi P. Sonar and Sakshi Dudhe and Suhani Harsule and Ashwini Paturkar},
        title = {Analysis and Overview of Printed Circuit Board Defect Detection Methods},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {869-873},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174666},
        abstract = {This paper provides an in-depth analysis and overview of various defect detection methods for Printed Circuit Boards (PCBs). A core focus is placed on feature extraction techniques, which are fundamental to accurate and reliable defect identification. The paper examines the evolution of these methods, from traditional image processing to machine learning and the now-dominant deep learning approaches, particularly Convolutional Neural Networks (CNNs). A detailed discussion of feature information is presented, encompassing handcrafted, learned (with pre-processing), and deep learning-derived features. The paper highlights how different methods represent and utilize feature information, along with recent trends and algorithms that enhance feature representation and improve PCB defect detection performance.},
        keywords = {},
        month = {April},
        }