To solve the problems of installation errors,time and labor consuming of manual detection,an image recognition model for single thermal battery defects based on transfer learning and convolutional neural network (CNN) is proposed.First,the images of the dataset are preprocessed by cropping and adding noise,etc.The visual geometry group 16 (VGG16) network is used as the backbone architecture of the model,and a selective kernel (SK) convolution is used after the bottleneck layer.Then,global average pooling (GAP) layer and Dropout layer are added,and L2 regularization and other fine-tuning operations are also added,an defect recognition model Q-VGGNet for single thermal battery is got.Finally,pre-training learning is performed on the dataset ImageNet,and the learned weight parameters are transferred to the model Q-VGGNet.The testing results show that the overall recognition accuracy of the six net-work models for the defect images on the dataset can reach 98.39%,94.44%,97.27%,96.34%,93.71% and 95.61%,respectively.The recognition accuracy rates of the Q-VGGNet network model for qualified images and the three types of defective images (negative electrode missing,tab broken,and current plate missing) can reach 99.6%,95.9%,99.6% and 98.4%,respectively.The results show that this method can detect thermal battery defects more accurately and quickly, and has good defect diagnosis ability.The accuracy is improved nearly 3% higher than the traditional method,and a good solution for manual detection of single thermal battery defects is provided.