Region Feature-Guided Automated Seed Selection Region Growing Method for Chip-Surface-Defect Detection

To overcome the limitations and inefficiencies associated with manual seed point selection in traditional region growing method for chip-surface-defect detection and to enhance automation and efficiency, this paper proposes a region feature-driven automated seed selection region growing method. By analyzing the local features of chip images, this method achieves automated seed point selection and, combined with the region growing method, accurately detects surface defects on chips. The main application algorithms studied in this paper include image preprocessing, regional feature extraction, automated seed point selection, region growing method, and morphological post-processing. Experimental results demonstrate that the proposed algorithm offers significant advantages in improving detection efficiency and accuracy. The detection speed for a single sample is reduced to less than 0.5 second, and the accuracy is improved to up to 93.3%, providing a new technological approach for chip-surface-defect detection.