[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.33851/JMIS.2021.8.4.211

SMD Detection and Classification Using YOLO Network Based on Robust Data Preprocessing and Augmentation Techniques

NDAYISHIMIYE, Fabrice (Department of Computer Engineering, Keimyung University)
Lee, Joon Jae (Department of Computer Engineering, Keimyung University)

Publication Information

Journal of Multimedia Information System / v.8, no.4, 2021 , pp. 211-220 More about this Journal

Abstract

The process of inspecting SMDs on the PCB boards improves the product quality, performance and reduces frequent issues in this field. However, undesirable scenarios such as assembly failure and device breakdown can occur sometime during the assembly process and result in costly losses and time-consuming. The detection of these components with a model based on deep learning may be effective to reduce some errors during the inspection in the manufacturing process. In this paper, YOLO models were used due to their high speed and good accuracy in classification and target detection. A SMD detection and classification method using YOLO networks based on robust data preprocessing and augmentation techniques to deal with various types of variation such as illumination and geometric changes is proposed. For 9 different components of data provided from a PCB manufacturer company, the experiment results show that YOLOv4 is better with fast detection and classification than YOLOv3.

Keywords

PCB inspection; SMD inspection; Classification; Detection; YOLO;

Citations & Related Records

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