雨雾环境下的变电站指针式仪表识别方法

Reading Method of Substation Pointer Meter in Rain-Fog Environment

目前巡检机器人在变电站室外巡检复杂环境下存在指针式仪表识别准确低的问题,提出了一种基于灰度级动态调整和Blackhat-Otsu算法的指针式仪表识别方法。针对雾天环境,提出了基于灰度级动态调整的Retinex去雾算法,对不同浓度的含雾图像进行了处理,提高了图像的对比度和清晰度,与其他去雾方法相比,所得图像的信息熵分别提升了1.1 dB~2 dB,均方误差(MSE)降低了700~800。在ResNet网络去雨模型中,引入快速引导滤波层以去除图像上的雨纹,峰值信噪比(PSNR)和结构相似性(SSIM)均有提升。为了提高指针读数的准确度,提出了Blackhat-Otsu指针分离法,避免了指针阴影及表盘刻度的干扰。实验结果表明,所提方法对变电站雨雾环境具有良好的鲁棒性,提升了仪表检测与读数识别的准确性。

When an inspection robot is applied in the outdoor substation, there exists a problem of low accuracy of pointer meter recognition in complex environment. This paper proposes a pointer meter recognition method based on gray-level dynamic adjustment and Blackhat-Otsu algorithm. Aiming at the foggy environment, the Retinex dehazing algorithm based on gray level dynamic adjustment is proposed to process foggy images with different concentrations and the image contrast and clarity are improved. The information entropy of the obtained image is increased by 1.1 dB--2 dB compared with that of other dehazing methods, but the mean square error(MSE) is reduced by 700--800. The fast guided filter layer is introduced in the ResNet network deraining model to remove the rain pattern on the image, and the peak signal-to-noise ratio(PSNR) and structural similarity(SSIM) are both improved. In order to improve the accuracy of pointer reading, the Blackhat-Otsu pointer separation method is proposed to eliminate the interference of pointer shadow and dial scale. The experimental results show that the proposed method has good robustness to the rain-fog environment in the substation, and improves the accuracies of instrumental detection and reading recognition.