Abstract
Manual inspection methods are performed by inspectors of the company to detect voids in single pad X-ray images using image processing. This manual procedure is subjective and time-consuming. The purpose of this project is to design a detection system for void areas in single pad X-ray images. Three stages of image processing are applied in this study. The image acquisition stage involves two activities namely single pad X-ray images acquisition and image editing activities. During the pre-image processing stage, the contrast of the single pad X-ray images are enhanced. The Adaptive Histogram Equalization (AHE) method has been found as the best technique for the enhancement stage. Finally, during the image processing stage, the segmentation process has been applied to detect the void regions. Then, the detected void regions have been distinguished from the other regions using the thresholding technique. Based on 20 single pad X-ray images, the qualitative analysis showed that the proposed void detection system has the capability to segment void regions, and this could assist company inspectors to improve inspection.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yu, Q., Shibutani, T., Kobayashi, Y., Shiratori, M.: The effect of voids on thermal reliability of BGA lead free solder joint and reliability detecting standard. In: Proceedings of 10th Intersociety Conference on Phenomena in Electronics Systems, Thermal and Thermomechanical, San Diego, California, 30 May–2 June 2006, pp. 1024–1030 (2006)
Truong, M.T., Kim, S.: Automatic image thresholding using Otsu’s method and entropy weighting scheme for surface defect detection. Softw. Comput. 22, 4197–4203 (2018)
Mei, S., Wang, Y., Wen, G.: Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model. Sens. Signal Process. Vis. Comput. 18, 1064 (2018)
Wang, T., Chen, Y., Qiao, M., Snoussi, H.: A fast and robust convolutional neural network-based defect detection model in product quality control. Int. J. Adv. Manuf. Technol. 94, 3465–3471 (2018)
Said, A.F., Bennett, B.L., Karam, L.J., Pettinato, J.S., Siah, A., Goodman, K.: Automated void detection in solder balls in the presence of vias and other artifacts. IEEE Trans. Compon. Packag. Manuf. Technol. 2(11), 1890–1901 (2012)
Mery, D.: Computer Vision for X-Ray Testing. Springer, Switzerland (2015). https://doi.org/10.1007/978-3-319-20747-6
Yuan, W., De-jian, Z.: The study on information extraction of BGA solders joint 2-D morphology based on wavelet modulus maximum. In: Proceedings of 12th International Conference on Electronic Packaging Technology and High Density Packaging, Shanghai, China, 8–11 August 2011 (2011)
Carrasco, M., Mery, D.: Automatic multiple view inspection using geometrical tracking and feature analysis in aluminium wheels. Mach. Vis. Appl. 22(1), 157–170 (2011)
Li, X., Tso, S.K., Guan, X.-P., Huang, Q.: Improving automatic detection of defects in castings by applying wavelet technique. IEEE Trans. Ind. Electron. 53(6), 1927–1934 (2006)
Acknowledgement
This project is under Memorandum of Agreement between Universiti Sains Malaysia and ViTrox Technologies Sdn. Bhd. This project is also partially supported by Research University (Individual) Grant with account number 1001/PELECT/8014030.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Norhairi, A.N.A., Isa, N.A.M., Sakim, H.A.M., Lim, L.N., Lim, S.Y. (2022). Detection of Void Regions in Single Pad X-ray Images Using Image Processing Approach. In: Mahyuddin, N.M., Mat Noor, N.R., Mat Sakim, H.A. (eds) Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 829. Springer, Singapore. https://doi.org/10.1007/978-981-16-8129-5_2
Download citation
DOI: https://doi.org/10.1007/978-981-16-8129-5_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8128-8
Online ISBN: 978-981-16-8129-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)