## 2 Method for Color Analysis of Objects with Complex Surface Conditions  
### The Selection of Samples and Their Conditions  
When the surface color of the object is too close, it is often necessary to use the ocular estimate method to classify the same surface color of the objects into a category in industrial production. According to different color objects will be classify into several categories of similar color to ensure the overall aesthetics of the same category of objects in subsequent production. For example, the production of a polycrystalline silicon photovoltaic module requires many polycrystalline silicon cells with the same color in the field of photovoltaic manufacturing. In the production of polycrystalline silicon cells, the surface of a polycrystalline silicon cell is irregular due to its polycrystalline properties. At the same time, due to the continuity of production, the thickness of its anti-reflection film cannot be controlled, which leads to the color differences of the cells surface, and the color differences from light blue to deep blue. This leads to the problem of color differences between cells in the same module in the production of photovoltaic modules, which greatly affects the integrity and aesthetics of polycrystalline silicon photovoltaic modules, and fails to meet the use requirements of landmark photovoltaic facilities, integration of photovoltaic buildings and other systems, and fails to meet the product requirements of high-end customers. Before making polycrystalline silicon cells into modules, a special production process is generally added, namely, color sorting of polycrystalline silicon cells. This process is done manually, which the workers identify the color difference of polycrystalline silicon cells by ocular estimate method on the basis of the color standard chip. There are four major problems with the existing process. First, different people's eyes perceive color differently, which may lead to errors in sorting. Second, after a lot of heavy color sorting, workers' eyes will gradually suffer from fatigue damage, which will aggravate human eye recognition errors. Third, the change of the light source in the workplace and other external factors can also lead to sorting errors. What's more, the color sorting process itself has no excessive technical requirements on workers and wastes a lot of unnecessary manpower. Therefore, it is very important to analyze the color of polycrystalline silicon cells and find a way to automaticallydistinguish the color of polycrystalline silicon cells. So polycrystalline silicon cells will be sampled to analyze the color discrimination method in this paper. Polycrystalline silicon cell production of Yingli Green Energy Holding Co., Ltd. is taken as an example. Polycrystalline silicon cells are classified into five categories roughly according to different colors by workers in the factory. From the lightest blue to the deepest blue, these five categories are generally named as LL, LQ, LZ, LS and R6, as shown in Figure 1. How does this color difference come about? There are two reasons for the difference in principle. First, we know that monocrystalline silicon cells do not have this color difference. The main difference between monocrystalline silicon cell and polycrystalline silicon cell is whether the crystal orientation of the entire cell is consistent. The crystal orientation of monocrystalline silicon cell is completely consistent, while polycrystalline silicon cell is composed of many small monocrystalline silicon grains, which are not consistent. So, the surface of a polycrystalline silicon cell is irregularity, unlike the consistent pyramid structure of a monocrystalline silicon cell (since silicon is a face-centered cubic crystal). The irregular surface of a polycrystalline silicon is one of the reasons for the color difference.