Dataset Citra Papan Sirkuit Tercetak dengan Komponen yang Terbakar

  • Iwan Awaludin Politeknik Negeri Bandung, Bandung, Indonesia
  • Trisna Gelar * Mail Politeknik Negeri Bandung, Bandung, Indonesia
  • Muhammad Rizqi Sholahuddin Politeknik Negeri Bandung, Bandung, Indonesia
  • Gina Melinia Politeknik Negeri Bandung, Bandung, Indonesia
  • Irvan Kadhafi Politeknik Negeri Bandung, Bandung, Indonesia
  • Rezky Wahyuda Sitepu Politeknik Negeri Bandung, Bandung, Indonesia
  • (*) Corresponding Author
Keywords: Dataset; Deep Learning; EfficientDet; Burnout Component; Printed Circuit Board


The application of artificial intelligence, especially in the automatic optical inspection of printed circuit boards or PCBs, is increasingly being carried out by researchers. Unfortunately, the data used to train and test artificial intelligence models is synthetic data. Printed circuit boards in good condition are imaged and then changed by software to give the impression of defects. In addition, the type of damage is limited to pre-operation, namely when the PCB is not yet operational. After the PCB is operational, damage can occur, for example, burned components. Until now, there is no data set of PCB images with burned components. This study, therefore, explores data retrieval techniques that can produce the required data set. This data collection technique includes hardware setup and PCB data sources. Based on the exploration results, it is concluded that a trinocular digital microscope with high resolution can produce sharp PCB images. The obstacle that arises is the difficulty of getting PCBs with burned components. The solution was obtained by referring to the PCB repair video from the Youtube channel. Several data were collected and tested with EfficientDet with 90% mAP.


Download data is not yet available.


B. Xia, J. Cao, and C. Wang, “SSIM-NET: Real-Time PCB Defect Detection Based on SSIM and MobileNet-V3,” in 2019 2nd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM), Nov. 2019, pp. 756–759. doi: 10.1109/WCMEIM48965.2019.00159.

Y.-S. Deng, A.-C. Luo, and M.-J. Dai, “Building an Automatic Defect Verification System Using Deep Neural Network for PCB Defect Classification,” in 2018 4th International Conference on Frontiers of Signal Processing (ICFSP), Sep. 2018, pp. 145–149. doi: 10.1109/ICFSP.2018.8552045.

S. Khalilian, Y. Hallaj, A. Balouchestani, H. Karshenas, and A. Mohammadi, “PCB Defect Detection Using Denoising Convolutional Autoencoders,” in 2020 International Conference on Machine Vision and Image Processing (MVIP), Feb. 2020, pp. 1–5. doi: 10.1109/MVIP49855.2020.9187485.

S. Tang, F. He, X. Huang, and J. Yang, “Online PCB Defect Detector On A New PCB Defect Dataset,” Feb. 2019.

W. Huang, P. Wei, M. Zhang, and H. Liu, “HRIPCB: a challenging dataset for PCB defects detection and classification,” The Journal of Engineering, vol. 2020, no. 13, pp. 303–309, Jul. 2020, doi: 10.1049/joe.2019.1183.

W. Huang and P. Wei, “A PCB Dataset for Defects Detection and Classification,” Jan. 2019.

J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” 2018. [Online]. Available:

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137–1149, Jun. 2017, doi: 10.1109/TPAMI.2016.2577031.

R. Ding, L. Dai, G. Li, and H. Liu, “TDD-net: a tiny defect detection network for printed circuit boards,” CAAI Transactions on Intelligence Technology, vol. 4, no. 2, pp. 110–116, Jun. 2019, doi: 10.1049/TRIT.2019.0019.

C. Pramerdorfer and M. Kampel, “A dataset for computer-vision-based PCB analysis,” in 2015 14th IAPR International Conference on Machine Vision Applications (MVA), May 2015, pp. 378–381. doi: 10.1109/MVA.2015.7153209.

G. Mahalingam, K. M. Gay, and K. Ricanek, “PCB-METAL: A PCB Image Dataset for Advanced Computer Vision Machine Learning Component Analysis,” in 2019 16th International Conference on Machine Vision Applications (MVA), May 2019, pp. 1–5. doi: 10.23919/MVA.2019.8757928.

L. Hangwei, M. Dhwani, P. Olivia, A. Navid, T. Mark, and Damon L. Woodard, “FICS-PCB: A Multi-Modal Image Dataset for Automated Printed Circuit Board Visual Inspection,” 2020, Accessed: Dec. 31, 2021. [Online]. Available:

NorthridgeFix, “NorthridgeFix - YouTube,” 2021. (accessed Dec. 31, 2021).

Livyu FPV, “Livyu FPV - YouTube,” 2021. (accessed Dec. 31, 2021).

G. Melinia, I. Kadhafi, dan R. W. Sitepu, “Sistem Pendeteksi Objek Kerusakan Burnout pada Modul PCB dengan Memanfaatkan Arsitektur Efficientdet,” Tugas Akhir, Politeknik Negeri Bandung, 2021.

X. Zou, “A Review of object detection techniques,” Proceedings - 2019 International Conference on Smart Grid and Electrical Automation, ICSGEA 2019, pp. 251–254, Aug. 2019, doi: 10.1109/ICSGEA.2019.00065.

Streamlit Inc., “Streamlit • The fastest way to build and share data apps,” 2021. (accessed Dec. 31, 2021).

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Dataset Citra Papan Sirkuit Tercetak dengan Komponen yang Terbakar

Article History
Submitted: 2021-12-14
Published: 2021-12-31
Abstract View: 18 times
PDF Download: 11 times
How to Cite
Awaludin, I., Gelar, T., Sholahuddin, M., Melinia, G., Kadhafi, I., & Sitepu, R. (2021). Dataset Citra Papan Sirkuit Tercetak dengan Komponen yang Terbakar. Building of Informatics, Technology and Science (BITS), 3(3), 179-185.