基于RetinaNet的变压器图像小部件智能识别方法

变电站巡检图像中变压器小部件的自动识别是利用变压器图像进行变压器外观异常缺陷识别的基础。为了提高变压器小部件的识别准确性,提出了一种基于RetinaNet的变压器小部件识别方法。首先,对图像目标检测网络Retina Net进行改进,加入分辨率更高的融合特征图,以解决变压器小部件包含的像素信息过少的问题。然后,提出一种基于位置关联性的变压器小部件概率修正方法,利用识别难度相对较小的大部件位置与相应的小部件之间的位置关联信息,对小部件检测框的预测概率进行修正,以避免其他外形相似部件对目标部件识别的干扰。最后,通过实际变电站巡检图像对变压器小部件识别方法进行实验验证。结果表明,所提出的变压器小部件识别方法在变压器三类小部件的识别准确率以及整体识别准确率上,都具有比较显著的优势。

Intelligent recognition method for transformer small components based on RetinaNet

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).