Scratch detection algorithm for concave optic elements based on hybrid strategy

The scratches on fiber optic imaging elements are predominantly detected manually, resulting in highly subjective inspection outcomes. To eliminate the influence of human subjectivity in detection, this study employs a platform equipped with multi-angle annular top lighting and backlighting to capture surface images of fiber optic imaging elements. A hybrid strategy-based image recognition algorithm is proposed. The algorithm first defines the image processing region using backlit images, filters out top-lit images containing large bright spots via grayscale histogram analysis, then detects scratches using an adaptive dual-threshold edge detection algorithm and a differential edge extraction method. The combined results from both processes undergo connected domain analysis to identify scratches. Experimental results indicate that the proposed algorithm achieves a scratch detection rate of 93%, while enabling rapid and effective detection of faint scratches.