Automatic recognition of transformer small components in substation inspection images is the basis of recognizing transformer exterior defects by transformer images. To improve the recognition accuracy, a recognition method based on RetinaNet is proposed. First, the object detection network RetinaNet is improved, and the fusion feature map with higher resolution is added to solve the problem that transformer small components contain too little pixel information. Then, a probability correction method for the components based on location correlation is proposed. This can modify the probability of small component detection boxes according to the location correlation between large components and corresponding small components. This avoids the interference of other similar components to the target component recognition. Finally, the proposed recognition method is verified by the actual substation inspection images, and the result shows that the method has significant advantages both in the recognition accuracy of three types of transformer small components and the overall recognition accuracy. This work is supported by the National Youth Science Foundation of China (No. 62001416) and the Science and Technology Project of State Grid Zhejiang Electric Power Co., Ltd. (No. 2019-HUZJTKJ-09).