A.A. Solodnyakov1, M.A. Krasnobaev2, A.V. Arkhipov3, D.A. Zakharikov4, K.V. Selivanov5
1–5 Bauman Moscow State Technical University (Moscow, Russia)

1 info@radiotec.ru

In modern mass production of printed circuit boards (PCB), quality control is a critically important task. Errors made by operators during manual inspection can lead to defective products passing through, significantly increasing correction costs and jeopardizing the reliability of the final product. This creates a demand for the development and implementation of automated defect detection systems capable of improving the speed and accuracy of quality control.

The aim of the article is to explore the potential of using the YOLOv4 architecture for automatic defect detection on PCBs during visual inspection. This includes developing data preparation methods, training the model, and evaluating its effectiveness under real production conditions.

The study results demonstrate that YOLOv4 achieves high accuracy and speed in detecting various types of PCB defects. Experimental findings show that implementing this technology significantly reduces the time required for quality control and decreases the likelihood of missing defects.

The practical significance of this work lies in the applicability of the developed methods in industrial settings, which can significantly enhance the quality control process at all stages of PCB production. Automating this process will reduce the impact of human factors and improve the overall efficiency of the production cycle.

Solodnyakov A.A., Krasnobaev M.A., Arkhipov A.V., Zakharikov D.A., Selivanov K.V. Application of YOLOv4 for defect detection on printed circuit boards: Methods, training, and results. Neurocomputers. 2025. V. 27. № 5. P. 23–34. DOI: https://doi.org/10.18127/ j19998554-202505-03 (in Russian)

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