Infrared camera can be used to monitor the condition of substation electrical equipment. Fast and accurate target detection algorithm is the key for infrared intelligent on-line routing inspection. However, the performance of traditional detection algorithm is poor due to the complex background of substation. To solve this problem, this paper proposes a target detection model based on the improved faster region-based convolutional neural networks (Faster R-CNN), which can be used for automatic detection of five kinds of electrical equipment in substations. The feature extraction network of this model is improved based on VGG16 by abandoning some high-level convolutions to accelerate the training and testing speed. Meanwhile, the 1:3 and 3:1 aspect ratio of anchor are added to improve the detection accuracy of elongated equipment. Experiments are performed on an infrared image dataset of substation to detect five types of equipment. The robustness tests of our model are carried out, too. The results show that our model performs well in detection accuracy and speed, achieving a mean detected accuracy of 95.32% and running at 11 frame/second. In addition, our model is robust to noise and lightness, which is suitable to other substations. Comparing to other models, our model has the highest mAP@0.5 of 92.78%.

中文翻译：

---

使用改进的 Faster R-CNN 进行变电站电气设备红外图像目标检测

红外摄像机可用于监测变电站电气设备的状况。快速准确的目标检测算法是红外智能在线巡检的关键。然而，由于变电站背景复杂，传统检测算法性能较差。针对这一问题，本文提出了一种基于改进的更快的基于区域的卷积神经网络（Faster R-CNN）的目标检测模型，可用于变电站五种电气设备的自动检测。该模型的特征提取网络在VGG16的基础上进行了改进，放弃了一些高层卷积，加快了训练和测试速度。同时增加了1:3和3:1的anchor长宽比，提高了对细长设备的检测精度。在变电站的红外图像数据集上进行了实验，以检测五种类型的设备。我们的模型也进行了稳健性测试。结果表明，我们的模型在检测精度和速度方面表现良好，平均检测精度达到 95.32%，运行速度为 11 帧/秒。此外，我们的模型对噪声和亮度具有鲁棒性，适用于其他变电站。与其他模型相比，我们的模型具有最高的 mAP@0.5，达到 92.78%。适用于其他变电站。与其他模型相比，我们的模型具有最高的 mAP@0.5，达到 92.78%。适用于其他变电站。与其他模型相比，我们的模型具有最高的 mAP@0.5，达到 92.78%。