An Effective Method for the Detection of Wall Brick Defects using Machine Vision

Authors

  • Ngoc-Tien Tran School of Mechanical and Automotive Engineering, Hanoi University of Industry, Vietnam
  • Ngoc-Duy Le School of Mechanical and Automotive Engineering, Hanoi University of Industry, Vietnam
  • Van-Nghia Le School of Mechanical and Automotive Engineering, Hanoi University of Industry, Vietnam
Volume: 14 | Issue: 3 | Pages: 14465-14469 | June 2024 | https://doi.org/10.48084/etasr.7503

Abstract

The production lines for wall bricks have achieved a high level of automation. Most brick production lines in developing countries have automated the steps up to placing the bricks in the kiln. However, the manual loading and unloading of bricks after firing still remains. This manual process reduces labor productivity and increases the cost of the final product. To address this issue, this study aims to utilize machine vision algorithms to detect cracks in bricks, thereby facilitating the automation of the brick loading and unloading process. A comprehensive image processing method is developed, which combines square detection and moment algorithms to analyze image properties. This integrated approach enables the accurate detection of cracks and the determination of their respective areas, ensuring precise and reliable results. By detecting defects in the bricks, we can replace faulty ones and employ robots to automatically handle rows of bricks. The study's results demonstrate the proposed method's ability to accurately identify brick defects. These findings are significant as they contribute to the automation of brick loading and unloading, which can be implemented in large-scale brick factories, leading to a safer and more efficient working environment.

Keywords:

machine vision, wall brick, brick crack, product defects

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References

H. Bilal, B. Yin, M. S. Aslam, Z. Anjum, A. Rohra, and Y. Wang, "A practical study of active disturbance rejection control for rotary flexible joint robot manipulator," Soft Computing, vol. 27, no. 8, pp. 4987–5001, Apr. 2023.

T.-L. Bui and N.-T. Tran, "Navigation Strategy for Mobile Robot Based on Computer Vision and Yolov5 Network in the Unknown Environment," Applied Computer Science, vol. 19, no. 2, pp. 82–95, Jun. 2023.

N.-T. Tran, T.-D. Ngo, D.-K. Nguyen, P. X. Son, and N. H. Thai, "Mapping and Path Planning for the Differential Drive Wheeled Mobile Robot in Unknown Indoor Environments Using the Rapidly Exploring Random Tree Method," in The AUN/SEED-Net Joint Regional Conference in Transportation, Energy, and Mechanical Manufacturing Engineering, 2022, pp. 516–527.

M. B. Ayed, L. Zouari, and M. Abid, "Software In the Loop Simulation for Robot Manipulators," Engineering, Technology & Applied Science Research, vol. 7, no. 5, pp. 2017–2021, Oct. 2017.

H. Medjoubi, A. Yassine, and H. Abdelouahab, "Design and Study of an Adaptive Fuzzy Logic-Based Controller for Wheeled Mobile Robots Implemented in the Leader-Follower Formation Approach," Engineering, Technology & Applied Science Research, vol. 11, no. 2, pp. 6935–6942, Apr. 2021.

J. Iqbal, "Modern Control Laws for an Articulated Robotic Arm: Modeling and Simulation," Engineering, Technology & Applied Science Research, vol. 9, no. 2, pp. 4057–4061, Apr. 2019.

M. Khojastehnazhand, M. Omid, and A. Tabatabaeefar, "Determination of orange volume and surface area using image processing technique," International Agrophysics, vol. 23, no. 3, pp. 237–242.

L. Babic, S. Matic-Kekic, N. Dedovic, M. Babic, and I. Pavkov, "Surface Area and Volume Modeling of the Williams Pear (Pyrus Communis)," International Journal of Food Properties, vol. 15, no. 4, pp. 880–890, Jul. 2012.

A. Agarwal and K. Goel, "Comparative Analysis of Digital Image for Edge Detection by Using Bacterial Foraging & Canny Edge Detector," in 2016 Second International Conference on Computational Intelligence & Communication Technology (CICT), Ghaziabad, India, Oct. 2016, pp. 125–129.

J. V. N. Lakshmi and R. Kamalraj, "Extracting Pixel Edges on Leaves to Detect Type Using Fuzzy Logic," in Computational Intelligence in Image and Video Processing, Chapman and Hall/CRC, 2023, pp. 33–54.

M. Versaci and F. C. Morabito, "Image Edge Detection: A New Approach Based on Fuzzy Entropy and Fuzzy Divergence," International Journal of Fuzzy Systems, vol. 23, no. 4, pp. 918–936, Jun. 2021.

D. Loverdos and V. Sarhosis, "Automatic image-based brick segmentation and crack detection of masonry walls using machine learning," Automation in Construction, vol. 140, Aug. 2022, Art. no. 104389.

M. Ravichand, R. Kumar, B. Hazela, and T. Suthar, "Crack on Brick Wall Detection by Computer Vision using Machine Learning," in 2022 6th International Conference on Electronics, Communication and Aerospace Technology, Coimbatore, India, Sep. 2022, pp. 1017–1020.

S. Katsigiannis, S. Seyedzadeh, A. Agapiou, and N. Ramzan, "Deep learning for crack detection on masonry façades using limited data and transfer learning," Journal of Building Engineering, vol. 76, Oct. 2023, Art. no. 107105.

M. G. Devereux, P. Murray, and G. M. West, "A new approach for crack detection and sizing in nuclear reactor cores," Nuclear Engineering and Design, vol. 359, Apr. 2020, Art. no. 110464.

Z. Y. Wu, R. Kalfarisi, F. Kouyoumdjian, and C. Taelman, "Applying deep convolutional neural network with 3D reality mesh model for water tank crack detection and evaluation," Urban Water Journal, vol. 17, no. 8, pp. 682–695, Sep. 2020.

M. H. Talukder, S. Ota, M. Takanokura, and N. Ishii, "Crack Detection on Brick Walls by Convolutional Neural Networks Using the Methods of Sub-dataset Generation and Matching," in Deep Learning Theory and Applications, 2023, pp. 134–150.

J. Zhang, S. Qian, and C. Tan, "Automated bridge surface crack detection and segmentation using computer vision-based deep learning model," Engineering Applications of Artificial Intelligence, vol. 115, Oct. 2022, Art. no. 105225.

H. Bae, K. Jang, and Y.-K. An, "Deep super resolution crack network (SrcNet) for improving computer vision–based automated crack detectability in in situ bridges," Structural Health Monitoring, vol. 20, no. 4, pp. 1428–1442, Jul. 2021. Cao, "Research on crack detection of bridge deck based on computer vision," IOP Conference Series: Earth and Environmental Science, vol. 768, no. 1, Feb. 2021, Art. no. 012161.

Y. Zhang, R. Hou, H. Wang, X. Chen, and P. Yang, "Numerical Simulation of Weak Parts of Main Components of Heavy-Duty Precision Brick Palletizing Robot," IOP Conference Series: Earth and Environmental Science, vol. 252, no. 2, Dec. 2019, Art. no. 022109.

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How to Cite

[1]
Tran, N.-T., Le, N.-D. and Le, V.-N. 2024. An Effective Method for the Detection of Wall Brick Defects using Machine Vision. Engineering, Technology & Applied Science Research. 14, 3 (Jun. 2024), 14465–14469. DOI:https://doi.org/10.48084/etasr.7503.

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