基于改进高斯卷积核的变电站设备红外图像检测方法

在无锚点算法CenterNet模型的基础上，针对基于红外图像的目标检测算法检测精度低、耗时长的问题，给出了一种基于改进高斯卷积核的变电站设备红外图像检测方法，该目标检测方法模型网络结构精简，模型计算量较小。通过现场变电站巡检机器人设备收集数据样本，进行算法模型的训练及验证，实现红外图像变电站设备精准识别及定位。本文以变电站巡检机器人搭配红外热成像仪采集到的红外图像库为基础，用深度学习方法对数据集进行训练和测试，研究变电站红外图像的目标检测技术。通过深度学习技术判断设备中心点位实现目标分类和回归。实验结果表明，该方法提高了变电站目标检测方法的识别定位精度，为变电站设备红外图像智能检测提供了新的思路。

Method of Detecting Substation Equipment in Infrared Images Based on Improved Gaussian Convolution Kernel

Slow and inaccurate target detection algorithms used to analyze infrared images are the focus of this study.An infrared image detection method is proposed for substation equipment using an improved Gaussian convolution kernel,which is based on the CenterNet algorithm without an anchor point.In brief,data samples were first collected using on-site substation inspection robot equipment,the algorithm model was trained and verified,and finally,accurate identification and positioning of infrared image substation equipment was achieved.Specifically,based on the infrared image library collected by the substation inspection robot and the infrared thermal imager,methods of deep learning were applied to train and test a model using the dataset,the target detection technology of substation infrared images was studied,and the equipment center was accurately judged through deep learning technology to achieve target classification and regression.The identification and positioning accuracy of the substation target detection were improved by adopting this proposed method,and it provides new ideas for the intelligent detection of infrared images for substation equipment.