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PCB Hata Tespit Görevlerinde YOLO11 Modellerinin Performans Analizi

Yıl 2025, Cilt: 2 Sayı: 1, 33 - 50, 27.06.2025

Mehmet Dayıoğlu , Ali Kemal Eyüboğlu , Ridvan Unal

Öz

Baskılı Devre Kartı (PCB) hata tespiti, elektronik üretiminde kritik bir süreçtir; tespit edilemeyen hatalar ciddi kalite kontrol problemlerine yol açabilir. Derin öğrenme ve özellikle nesne tespiti modellerindeki son gelişmeler, denetim sistemlerinin doğruluk ve hızında önemli iyileştirmeler sağlamıştır. Bu çalışma, çok sınıflı bir PCB arıza tespit veri kümesi üzerinde YOLO11 (You Only Look Once versiyon 11) nesne tespiti mimarisinin performansını incelemektedir. YOLO11 ailesine ait beş farklı varyant—YOLO11n, YOLO11s, YOLO11m, YOLO11l ve YOLO11x—altı farklı arıza türü içeren yüksek çözünürlüklü görüntüler kullanılarak, aynı eğitim koşulları altında değerlendirilmiştir. Değerlendirme için mAP@50, mAP@50-95 ve FPS gibi metrikler kullanılmıştır. Sonuçlar, YOLO11l modelinin 0.551 mAP@50-95 ile en yüksek doğruluk değerine ulaştığını, YOLO11n modelinin ise NVIDIA A100 GPU üzerinde saniyede 166 kareye (FPS) kadar gerçek zamanlı performans sergilediğini göstermektedir. Yapılan karşılaştırmalar, YOLO11 ailesinin doğruluk ve verimlilik arasında etkili bir denge sunduğunu doğrulamaktadır. Bu çalışma, YOLO11 mimarisinin gerçek zamanlı PCB denetim sistemleri için güçlü bir aday olduğunu ortaya koymaktadır.

Anahtar Kelimeler

PCB Hata Tespiti Derin Öğrenme YOLO11

Kaynakça

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Performance Analysis of YOLO11 Models in PCB Defect Detection Tasks

Yıl 2025, Cilt: 2 Sayı: 1, 33 - 50, 27.06.2025

Mehmet Dayıoğlu , Ali Kemal Eyüboğlu , Ridvan Unal

Öz

Printed Circuit Board (PCB) defect detection is critical in electronics manufacturing, as undetected faults can lead to severe quality control issues. Recent advancements in deep learning, particularly object detection models, have significantly improved inspection systems' accuracy and speed. This study explores the performance of the YOLO11 (You Only Look Once version 11) object detection architecture on a multi-class PCB defect dataset. Five YOLO11 variants—YOLO11n, YOLO11s, YOLO11m, YOLO11l, and YOLO11x—were trained and evaluated under identical conditions using high-resolution images containing six defect types. Metrics such as mAP@50, mAP@50-95, and FPS were used for evaluation. Results demonstrate that YOLO11l achieved the highest mAP@50-95 of 0.551, while YOLO11n achieved up to 166 Frame Per Second (FPS) on an NVIDIA A100 GPU, confirming its real-time capability. Comparative analysis against state-of-the-art models confirms that YOLO11 variants offer an effective trade-off between accuracy and efficiency. This study positions YOLO11 as a strong candidate for real-time PCB inspection systems.

Anahtar Kelimeler

PCB Defect Detection Deep Learning YOLO11

Kaynakça

  • [1] V. K. Ancha, F. N. Sibai, V. Gonuguntla, R. Vaddi, (2024). Utilizing YOLO models for real-world scenarios: Assessing novel mixed defect detection dataset in PCBs, IEEE Access. 12, 100983–100990. https://doi.org/10.1109/ACCESS.2024.3430329.
  • [2] Q. Ling, N.A.M Isa, (2023). Printed circuit board defect detection methods based on image processing, machine learning and deep learning: A survey, IEEE Access. 11, 15921–15944. https://doi.org/10.1109/ACCESS.2023.3245093.
  • [3] K. Singh, S. Kharche, A. Chauhan, P. Salvi, (2024). PCB defect detection methods: A review of existing methods and potential enhancements, Journal of Engineering Science & Technology Review. 17(1), 156-167. https://doi.org/10.25103/jestr.171.19.
  • [4] I.-C. Chen, R.-C. Hwang, H.-C Huang, (2023). PCB defect detection based on deep learning algorithm, Processes. 11(3), 775. https://doi.org/10.3390/pr11030775.
  • [5] L. Cai, J. Li, (2022). PCB defect detection system based on image processing. Journal of Physics: Conference Series, Qingdao, China, Conf. Ser. 2383, pp. 012077. https://doi.org/10.1088/1742-6596/2383/1/012077.
  • [6] G. Zhang, Y. Cao, (2023). A novel PCB defect detection method based on digital image processing. Journal of Physics: Conference Series, Suzhou, China, Conf. Ser. 2562, pp. 012030. https://doi.org/10.1088/1742-6596/2562/1/012030.
  • [7] S. H. I. Putera, Z. Ibrahim, (2010). Printed circuit board defect detection using mathematical morphology and MATLAB image processing tools. 2nd International Conference on Education Technology and Computer, Shanghai, China, pp. 359. https://doi.org/10.1109/ICETC.2010.5530052.
  • [8] M. Baygin, M. Karakose, A. Sarimaden, E. Akin, (2017). Machine vision based defect detection approach using image processing. 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), Malatya, Turkey, pp. 1–5. https://doi.org/10.1109/IDAP.2017.8090292.
  • [9] H. Hagi, Y. Iwahori, S. Fukui, Y. Adachi, M. K. Bhuyan, (2014). Defect classification of electronic circuit board using SVM based on random sampling, Procedia Computer Science. 35, 1210–1218. https://doi.org/10.1016/j.procs.2014.08.218.
  • [10] P. P. Londe, S. A. Chavan, (2014). Automatic PCB defects detection and classification using MATLAB, International Journal of Current Engineering and Technology. 4(3), 31–36.
  • [11] E. H Yuk, S. H. Park, C.-S. Park, J.-G. Baek, (2018). Feature-learning-based printed circuit board inspection via Speeded-Up Robust Features and Random Forest, Applied Sciences. 8(6), 932. https://doi.org/10.3390/app8060932.
  • [12] Y. Li, S. Li, (2017). Defect detection of bare printed circuit boards based on gradient direction information entropy and uniform local binary patterns, Circuit World. 43(4), 145–151. https://doi.org/10.1108/CW-06-2017-0028.
  • [13] J. Niu, J. Huang, L. Cui, B. Zhang, A. Zhu, (2022). A PCB defect detection algorithm with improved Faster R-CNN. International Conference on Big Data Applications and Services Engineering (ICBASE), Guangzhou, China, pp. 283–292.
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  • [24] M. Liang, J. Wu, H. Cao, (2022). Research on PCB small target defect detection based on improved YOLOv5. 2022 International Conference on Sensing, Measurement & Data Analytics in the Era of Artificial Intelligence (ICSMD), Harbin, China, pp. 1–5. https://doi.org/10.1109/ICSMD57530.2022.10058458.
  • [25] Y. Zhang, H. Zou, J. Wang, Z. Lei, M. Zhou, (2023). Lightweight neural network-based real-time PCB defect detection system. 2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), Yibin, China, pp. 1–6. https://doi.org/10.1109/SAFEPROCESS58597.2023.10295891.
  • [26] Q Li, L. Wu, H. Xiao, C. Huang, (2024). PCB-DETR: A detection network of PCB surface defect with spatial attention offset module, IEEE Access. 12, 158436–158445. https://doi.org/10.1109/ACCESS.2024.3486176.
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  • [28] X. Huang, W. Li, (2024). A novel PCB defect detection network based on the improved YOLOv8 with fusion of hybrid attention transformer and bidirectional feature pyramid network. 2024 4th International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC), Xiamen, China, pp. 207–211. https://doi.org/10.1109/ICAIRC64177.2024.10900305.
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Toplam 54 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Elektronik
Bölüm Araştırma Makalesi
Yazarlar

Mehmet Dayıoğlu 0000-0001-8323-0730

Ali Kemal Eyüboğlu 0000-0001-7637-2327

Ridvan Unal 0000-0001-6842-7471

Yayımlanma Tarihi 27 Haziran 2025
Gönderilme Tarihi 29 Nisan 2025
Kabul Tarihi 11 Haziran 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 2 Sayı: 1

Kaynak Göster

IEEE M. Dayıoğlu, A. K. Eyüboğlu, ve R. Unal, “Performance Analysis of YOLO11 Models in PCB Defect Detection Tasks”, KETBTD, c. 2, sy. 1, ss. 33–50, 2025.
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