Research On Optimization Of Adaptive Edge Detection Algorithm Based On Spatial Gradient Information

As the clean resource, solar energy has been widely used in various field. Solar cell is one of the core components of photovoltaic power generation system, and its quality has direct impact on the efficiency of the whole photovoltaic power generation system. Polysilicon substrate is widely used in photovoltaic power generation system. Photoelectric conversion efficiency of solar cell can be improved by promoting the crystallinity of polysilicon. It is important to set up the quality grade of the polysilicon substrate in production. At present, the detection means of polycrystalline silicon cells crystallinity is mainly sampling visual detection. The existing algorithms have been optimized and an adaptive edge algorithm based on spatial gradient information is proposed in this thesis. Firstly, the parameters are determined adaptively according to the images characteristics in the improved segmentation method combining bimodal method and Otsu' method. Secondly, the gradient edge is extracted by using spatial gradient information, so that the discontinuous edge points can be connected and the pseudo-edge can be removed by combining the gradient edge and initial edge. It is found that the proposed algorithm is simple and simulation results show that complete and continuous edges can be extracted for images with various characteristics. It lays a foundation for domain contour recognition and polysilicon crystallinity online detection.