A method for detecting defects of monomer thermal battery inside the thermal battery is proposed in this paper, which aimed at the problem of the low accuracy of internal assembly fault detection at home and abroad. This detection includes three defects, the overall flipchip of the monomer thermal battery, the assembly sequence of the monomer thermal battery, and the leakage of the monomer thermal battery part are analyzed. Using the improved gray level cooccurrence matrix, HU invariant moment, template matching to analyze the defects of monomer thermal batteries. Finally, proposing a detection method based on weight distribution parameters, which is using CART (Classification and Regression Tree) decision tree for detection. The experimental results show that the accuracy of this method reaches 975% and meets the testing requirements, which provides an effective way for thermal battery defect detection.