Aiming at the problems of low efficiency and low accuracy in the detection of internal assembly defects of thermal batteries, a method which could accurately segment the internal battery stack images and accurately identify the types of defects was studied. Firstly, the horizontal and vertical integral projection methods were used to extract the edge features of the target battery stack, and the local adaptive contrast enhancement algorithm was used to enhance the detail texture of the local unclear parts. Then, the gray characteristics of the defect structure were studied and the defect characteristic parameters were calculated and extracted. Finally, the BP neural network and CART decision tree were used to classify and identify the feature parameters, the weight was allocated according to the classification accuracy, and the weighted fusion results were used as the final criterion of the detection. The experimental results show that the accuracy of this method is 98.9% for 2 000 samples, which provides an effective way for X-ray defects detection of thermal batteries.