Recognition algorithm of internal defect images of thermal battery北大核心CSCD

针对目前热电池内部装配缺陷检测效率低、准确度不高的问题，研究了一种可精准分割内部电池堆图像并能够准确识别缺陷种类的方法。首先采用水平、垂直积分投影法对目标电池堆边缘特征进行提取，利用局部自适应对比度增强算法对局部不清晰部分进行细节纹理增强；然后研究了缺陷结构的灰度特性，计算提取出缺陷特征参数；最后使用BP（back propagation）神经网络和CART（classification and regression tree）决策树对特征参数分类识别，并根据分类准确度进行权重分配，将加权融合后的结果作为检测的最终判据。实验结果表明:该方法对2 000个样本的检测准确度达98.9%，为热电池的X射线缺陷检测提供了有效的途径。

Recognition algorithm of internal defect images of thermal battery

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.