为增强电气设备红外热图像的视觉效果,对其运行状态进行准确检测,提出了一种新型阈值选择的图像分割方法。该方法首先对原始图像进行傅里叶滤波处理形成自动梯度图形,然后针对每种特定类型的目标图像,拟合具有N个相邻点的线性模型计算斜率差的变化趋势,在斜率差分布谷值中挑选适合不同类型故障区域的最佳阈值,最后通过形态学迭代腐蚀,将目标区域与噪声斑点分开,得到清晰的分割图像。该方法可监测各种类型故障,只需校准参数N和确定分割案例,其余部分自动处理。实验结果显示:该方法对目标区域分割的准确率为82%,误分率为0.0182%。通过使用不同类型的红外热故障图像进行测试对比,验证了所提方法的有效性和通用性。

To enhance the visual effect of the infrared thermal image of electrical equipment and accurately detect its operating state, an image segmentation method based on a new threshold algorithm is proposed. First, the method performs Fourier filtering on the original image to form an automatic gradient graph. Then, for each specific type of fault distribution, the line model with N adjacent points is fitted to calculate the slope difference to find the optimal threshold for different types of fault regions. Finally, by morphological iterative etching, the target area is separated from the noise spots to obtain a clear segmentation image. This method is suitable for various fault types, which only requires calibration of N and determination of different segmentation cases, with others being processed automatically. The results show that the segmentation accuracy of this method is 85%, and the error rate is 0.0182%. The effectiveness and versatility of the proposed method are verified by using different types of infrared thermal fault images.