基于图像增强与深度学习的变电设备红外热像识别方法

Infrared Thermal Image Recognition of Substation Equipment Based on Image Enhancement and Deep Learning

红外热像的自动识别是变电设备缺陷与故障诊断的重要手段。针对目前变电设备的红外热像识别存在的极易受到背景杂波干扰、图像视觉效果差、缺乏智能方法等问题,使用快速导向滤波在去噪时保留边缘信息,提出参数自调整的Retinex算法对图像进行增强,提高红外热像的对比度;改进YOLOv3网络的特征提取网络与损失函数提高变电设备的识别精度。经测试,5种变电设备的识别平均准确率可以达到94.85%,每张图片的识别速度为7.88ms/张。实验结果表明了该方法的准确性和快速性,为实现变电设备状态监测提供一定条件。

Automatic recognition of infrared thermal image is an important means of the defect and fault diagnosis of substation equipment.Aiming at the problems of substation equipment detection,such as being extremely vulnerable to background clutter interference,image visual effects and lack of intelligent methods,fast guided filtering was used to retain edge information when removing noise.A parameter self-adjusting Retinex algorithm was proposed to enhance image contrast and an improved YOLOv3 network was presented to increase the recognition accuracy of substation equipment.After testing,the mAP of five kinds of substation equipment can reach 94.85%,and the recognition speed of each picture is 7.88ms.The experimental results show the accuracy and rapidity of the proposed method,which provides conditions for realizing the status monitoring of substation equipment.