Image Processing Techniques for Crack Detection in MPI of Springs

This study investigates image processing techniques for detecting surface cracks in spring steel components, with a focus on applications like Magnetic Particle Inspection (MPI) in industries such as railways and automotive. The research details a comprehensive methodology that covers data collection, software tools, and image processing methods. Various techniques, including Canny edge detection, Hough Transform, Gabor Filters, and Convolutional Neural Networks (CNNs), are evaluated for their effectiveness in crack detection. The study identifies the most successful methods, providing valuable insights into their performance. The paper also introduces a novel batch processing approach for efficient and automated crack detection across multiple images. The trade-offs between detection accuracy and processing speed are analyzed for the Morphological Top-hat filter and Canny edge filter methods. The Top-hat method, with thresholding after filtering, excelled in crack detection, with no false positives in tested images. The Canny edge filter, while efficient with adjusted parameters, needs further optimization for reducing false positives. In conclusion, the Top-hat method offers an efficient approach for crack detection during MPI. This research offers a foundation for developing advanced automated crack detection system, not only to spring sector but also extends to various industrial processes such as casting and forging tools and products, thereby widening the scope of applicability.