Abstract

Maintaining consistent product quality is a major challenge in modern manufacturing, especially in industries where surface integrity directly affects performance and reliability. Traditional inspection methods largely depend on human observation, which can be slow, subjective, and prone to inconsistency. This paper presents an automated image-based system for detecting surface defects using digital image processing techniques, aiming to improve accuracy and reduce manual effort. The proposed approach focuses on analysing surface images through a structured processing pipeline. Initially, images are enhanced to improve contrast and visibility of fine details. Noise reduction techniques are applied to eliminate unwanted variations, followed by segmentation methods to isolate potential defect regions. Edge detection and texture analysis are then used to identify irregular patterns such as scratches, cracks, and surface distortions. Instead of relying on a single technique, the system combines multiple feature extraction methods to improve robustness under varying lighting and surface conditions. The system is evaluated on different types of surface images, demonstrating reliable detection performance with minimal false detection. The results indicate that the proposed method can effectively distinguish between normal and defective regions even in complex scenarios. This work provides a practical and scalable solution for automated quality inspection by improving detection consistency, accuracy, and efficiency compared to manual inspection methods. The proposed framework can be adapted to different industrial applications with minimal modifications, making it suitable for diverse surface inspection and quality control environments.

References

• Bovik, A. C. (Ed.). (2005). Handbook of image and video processing. Academic Press.
• Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679–698.
• Chan, T. F., & Vese, L. A. (2001). Active contours without edges. IEEE Transactions on Image Processing, 10(2), 266–277.
• Costa, A. F., Humpire-Mamani, G., & Traina, A. J. (2012). An efficient algorithm for fractal analysis of textures. IEEE International Conference on Image Processing.
• Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. IEEE Conference on Computer Vision and Pattern Recognition.
• Davies, E. R. (2012). Computer and machine vision: Theory, algorithms, practicalities. Academic Press.
• Gonzalez, R. C., & Woods, R. E. (2018). Digital image processing (4th ed.). Pearson.
• Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 610–621.
• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. IEEE CVPR.
• Jain, A. K. (1989). Fundamentals of digital image processing. Prentice Hall.
• Kang, H., et al. (2016). Automatic defect detection of steel surfaces. IEEE Transactions on Instrumentation and Measurement.
• Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). ImageNet classification with deep CNNs. NIPS.
• Liao, P. S., Chen, T. S., & Chung, P. C. (2001). A fast algorithm for multilevel thresholding. Journal of Information Science and Engineering.
• Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision.
• Mallat, S. (1989). A theory for multiresolution signal decomposition. IEEE Transactions on Pattern Analysis and Machine Intelligence.
• Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics.
• Park, S., & Kang, H. (2015). Surface defect detection using image processing. Journal of Manufacturing Systems.
• Pham, D. T., & Alcock, R. J. (2003). Smart inspection systems. Proceedings of the Institution of Mechanical Engineers.
• Pratt, W. K. (2007). Digital image processing (4th ed.). Wiley.
• Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN. IEEE Transactions on Pattern Analysis and Machine Intelligence.
• Russ, J. C. (2016). The image processing handbook. CRC Press.
• Szeliski, R. (2010). Computer vision: Algorithms and applications. Springer.
• Tsai, D. M., & Hsieh, C. Y. (1999). Automated surface inspection using wavelet reconstruction. Pattern Recognition.
• Unser, M. (1995). Texture classification and segmentation. IEEE Signal Processing Magazine.
• Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. (2004). Image quality assessment. IEEE Transactions on Image Processing.
• Xie, X. (2008). A review of recent advances in surface defect detection. Pattern Recognition.
• Yadav, R., & Nishchal, N. (2018). Defect detection using image processing. International Journal of Computer Applications.
• Zhang, Y., & Wu, L. (2012). Classification of fruits using computer vision. Computers and Electronics in Agriculture.
• Zhang, D., & Lu, G. (2004). Review of shape representation and description techniques. Pattern Recognition.
• Zhao, Y., & Wang, S. (2019). Automated visual inspection system for surface defects. IEEE Access.