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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) Sinergi ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Reviewer TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science International Journal of Electrical, Energy and Power System Engineering (IJEEPSE) Indonesian Journal for Social Responsibility Journal of Integrated and Advanced Engineering (JIAE) TECHNOLOGIC
Andi Adriansyah
Universitas Mercu Buana
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences

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Journal : International Journal of Electrical and Computer Engineering

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Object detection in printed circuit board quality control: comparing algorithms faster region-based convolutional neural networks and YOLOv8 Kustija, Jaja; Fahrizal, Diki; Nasir, Muhamad; Adriansyah, Andi; Muttaqin, Muhammad Husni
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2796-2808

Abstract

Along with the development of electronic technology, the integration of numerous components on printed circuit board (PCB) boards has resulted in increasingly complex and intricate layouts. Small defects in traces can lead to failures in electronic functions, making the inspection of PCB surface layouts a critical process in quality control. Given the limitations of manual inspection, which struggles to detect such defects due to their size and complexity, there is a growing need for a PCB inspection system that utilizes automated optical inspection (AOI) based on deep learning detection. This research develops and compares two deep learning algorithms, faster region-based convolutional neural networks (R-CNN) and YOLOv8, to identify the most effective algorithm for detecting defects on PCB layouts. The findings of this study indicate that the YOLOv8 algorithm outperforms faster R-CNN, with the YOLOv8x variant emerging as the best model for defect detection. The YOLOv8x model achieved performance scores of 0.962 (mAP@50), 0.503 (mAP@50:95), 0.953 (Precision), 0.945 (Recall), and 0.949 (F1-score). These results provide a strong foundation for further research into the application of AOI for PCB defect detection and other quality control processes in manufacturing, using optimized deep learning models.
Co-Authors Ab Ghani, Muhammad Amin Abdi Wahab Amrullah , Ahmad Ghozali Annisa Hakim Zamzami Dafit Feriyanto Elly Yuliawati Erna Setiany Fahrizal, Diki Ferdana, Nanda Firdaus, Ade Hadi Pranoto Haekal, Jakfat Hitimana, Sabin Jaja Kustija, Jaja Julpri Andika KOERNIAWAN, SETYA DWI Malik, Mochamad Irlan Md Nor, Danial Mirzanu Rizki GM Mohamed Nasir, Noorhamizah Muhamad Nasir, Muhamad Muhammad Hafizd Ibnu Hajar Muttaqin, Muhammad Husni Pratiwi, Riri Rahayu, Muthia Ramadhan, Kurnia Rino Ferdian Surakusumah Rizal Bahaweres Rizki Briandana Selomah, Mohammad Suhaimi Shamsudin, Abu Ubaidah Shamsudin, Abu Ubaidillah Suhartina, Rahmalisa Suwoyo, Heru Tay, Tay Gaik Thong, Zhou Tian, Yingzhong Tjetjep Rony Budiman Yananto Mihadi Putra Yanti Yanti Yuliza . Zakaria, Supaat Zendi Iklima ZULHAMIDI, ZULHAMIDI Zulkifli, Muhammad Fathi Yakan