图像边缘检测算法对变电设备智能组件环境监测

Image Edge Detection Algorithm for Environment Monitoring of Substation Equipment Intelligent Components

由于变电设备智能组件在复杂高压的环境中运行,易发生火灾等安全事故,因此为了对变电设备智能组件进行安全监测,提出基于自适应阈值小波的图像边缘检测算法对变电设备智能组件发生火灾时进行图像去噪和火灾轮廓特征提取,有效提高火灾报警性能。首先建立多目视觉传感器对变电设备现场进行图像采集,然后对采集的图像首先经过Rgb2gray颜色转换,然后通过融合自适应阈值小波滤波,消除冗余障碍物信息干扰,最后通过改进的边缘检测Boundaries算法对火灾二值图边缘轮廓进行检测。实验表明建立的自适应阈值小波的图像边缘检测算法可以提高火灾图像去噪性能和火灾边缘轮廓检测的效果。

Because intelligent components of substation equipment operate in a complex high-voltage operating environment,they are prone to safety accidents such as fire.For the safety monitoring of substation equipment intelligent components,an image edge detection algorithm based on adaptive threshold wavelet is proposed to carry out image denoising and fire contour feature extraction in case of fire of substation equipment intelligent components.First,heterogeneous multi-vision sensors collect pictures on the scene of the substation equipment,then the collected images are first subjected to Rgb2 gray color conversion,and then the adaptive threshold wavelet filter is integrated to eliminate redundant information interference.Finally,the improved boundary detection Boundaries algorithm is used to detect the boundary contour of the binary fire map.Experiments show that the established edge image detection algorithm of adaptive threshold wavelet can improve the performance of fire image denoising and fire edge contour detection.