The weak light-leaking phenomenon of fibers will definitely decrease the output precision and working reliability of the Interferometric Fiber Optic Gyroscope (IFOG). Unfortunately, because of the small scratches or damages on the surface of fiber cannot be avoided when manufacturing IFOG, it is necessary to develop a method to monitor and evaluate that light leakage phenomenon and harm. In this paper, to evaluate and control that light-leaking defect, on one hand, a lightleaking energy function, which uses the image analysis method and the radiometry theory, is designed to represent the energy loss of light; on the other hand, we build a relationship between that energy function and the assembling quality of IFOG by Support Vector Machine (SVM): (1) we utilize an infrared CCD camera to make a traversal scan of the fibers inside of IFOG. (2) We use graph cut model and flood fill algorithm to make a segmentation of that light-leaking region. (3) We define a new energy function to estimate the light leakage level. (4) We use SVM to build a connection between the light-leaking energy function and the final output of IFOG. Here the image energy function is regarded as the training data of SVM, while the bias of IFOG is treated as the supervising data of it. A practical application case of IFOG light path quality evaluation is shown in the end of this paper.
To improve the assembly reliability of Fiber Optic Gyroscope (FOG), a light leakage detection system and method is developed. First, an agile movement control platform is designed to implement the pose control of FOG optical path component in 6 Degrees of Freedom (DOF). Second, an infrared camera is employed to capture the working state images of corresponding fibers in optical path component after the manual assembly of FOG; therefore the entire light transmission process of key sections in light-path can be recorded. Third, an image quality evaluation based region segmentation method is developed for the light leakage images. In contrast to the traditional methods, the image quality metrics, including the region contrast, the edge blur, and the image noise level, are firstly considered to distinguish the image characters of infrared image; then the robust segmentation algorithms, including graph cut and flood fill, are all developed for region segmentation according to the specific image quality. Finally, after the image segmentation of light leakage region, the typical light-leaking type, such as the point defect, the wedge defect, and the surface defect can be identified. By using the image quality based method, the applicability of our proposed system can be improved dramatically. Many experiment results have proved the validity and effectiveness of this method.