Defect Detection and Localization in Fiber-Optic Panels Based on Discrete Wavelet and Multiobjective Morphological Optimization

The concentric optical camera based on the optical fiber relay image can meet the needs of the miniaturization, lightweight, and large field-of-view detection of the space target detection system. However, due to the inevitable thermal stretching and torque processes involved in the production of fiber-optic panels (FOPs), defects of unknown shape, size, and location are formed, increasing the difficulty of space target information acquisition and causing center-of-mass localization errors. To efficiently and accurately detect and locate defects in FOPs, we propose an improved defect detection and localization method for FOPs based on discrete wavelet and multiobjective morphological optimization by using the gray value of the image sensor. We compare this method with the Canny edge detection method and the fuzzy C-means (FCM) clustering method and find that the method has higher detection accuracy and stronger robustness in an environment with a large number of dark filament interferences, and the detection results are not affected by the various image sizes of the FOPs. The method also maintains a high degree of consistency in detection metrics, with the F_Score detection accuracy of 85%. This research provides a reliable solution for FOP defect detection and localization, lays the foundation for image quality improvement of concentric optical cameras based on fiber-optic relay imaging, and contributes to space target detection and localization.