Automated Crack Detection Using Deep Learning and Zernike Moments

The patent replaces manual mechanical observation and measurement with an automated digital image processing system. The system uses image acquisition devices to capture crack images and automatically processes these images through computer algorithms to detect, draw, and measure cracks, eliminating the need for manual intervention and significantly improving detection efficiency while reducing time consumption.

The patent employs deep learning technology that automatically learns and adapts parameters for crack detection. The system trains neural networks on large datasets to automatically adjust detection parameters and features, enabling high precision crack identification even under varying backgrounds and lighting conditions, while the automated parameter learning reduces the need for manual parameter tuning.

The patent implements a self-service automated system where the deep learning model automatically performs crack detection, drawing, and measurement without human intervention. The system self-trains on datasets, automatically adjusts parameters, and produces detection results autonomously, achieving high automation levels while managing complexity through integrated software modules.

The patent transitions from simple pixel counting to a more sophisticated measurement approach that considers multiple dimensions. The system uses automated image processing algorithms that analyze crack geometry, orientation, and context across multiple image processing stages, enabling precise measurement of micro-cracks while maintaining measurement efficiency through automated computation.