Research on Feature Extraction and Classification Methods to Improve the Recognition Rate of Monomers Assembly Defects in Thermal Battery

In the assembly process of thermal battery monomers, problems such as inversion, wrong order, and missing collectors often occur. Defect detection is important for the normal use of thermal batteries. In order to improve the defect recognition rate, this paper proposes a feature extraction and classification method based on x-ray images. A new method is formed by combining Gray Level Co-occurrence Matrix and Local Binary Pattern, and improving the traditional Random Forest. Extract the gray texture of the monomer respectively by the Improved Gray Level Co-occurrence Matrix and Local Binary Pattern Equivalent Mode. Analyze the extracted results for serial feature fusion. The classification experiments are performed by Particle Swarm Optimized Random Forest Algorithm. The experimental results show that this method's final defect recognition rate is 98.9%. It provides a new way to identify thermal battery defects accurately and is of great significance in improving the thermal battery defect identification rate.

Automated summary

This paper proposes a feature extraction and classification method based on x-ray images to improve the defect recognition rate of thermal batteries. By combining the Gray Level Co-occurrence Matrix and Local Binary Pattern, the gray texture of the monomer is extracted and analyzed for serial feature fusion. The experimental results show that this method achieves a defect recognition rate of 98.9%.