摘要
受限于复杂的电磁环境,变电站中的大量模拟式仪表需要人工读取示数,不利于变电站自动化管理。而目前针对仪表自动读数方法的研究大多基于预先获取到的高质量图像,其中仪表目标位于图像中央且占比较大,仪表表盘与相机平面平行,这需要大量预先的仪表测量与相机标定工作,不能满足实际电站环境下的使用要求。为解决上述问题,提出了一种完整的变电站指针式仪表的自动检测与识别方法。首先利用卷积神经网络模型检测当前视野下仪表目标的包围框位置,计算其距离视野中央的偏离值与图像占比,据此调整相机位置和缩放倍数。通过透视变换消除表盘平面与相机平面偏差造成的仪表图像畸变,通过霍夫变换检测仪表的表盘与指针,完成仪表读数识别。变电站实际测试实验结果表明,本方法最大读数误差仅为1.82%,对于复杂背景下多类别仪表的自动检测与识别任务具有良好的准确性与稳定性,可满足变电站实际应用需求。
There are a large number of analogy meters due to complex electromagnetic environment in transformer substation and these meters need manual reading, which makes it difficult to automated manage transformer substation. Currently, most meter automatic reading methods rely on pre-acquired high quality image, in which meter targets are big in size and locate in the middle and surface of meter is parallel with the camera. This needs lots of prior meter measurement and camera calibration, which fails to meet the requirements of actual use in transformer substation. In order to solve the problem mentioned, this paper presents a complete meter detection and recognition method. First, meter location within current visual field is obtained through a convolutional neural network model. Then the difference between target center location and camera visual field center location, as well as size percentage of target, are calculated. Camera state, including camera location and camera scaling factor, is adjusted according to the calculation result. After that, high quality image of meter target is acquired through perspective transform, which eliminates the image distortion caused by non- parallelism between meter and camera. Finally, locations of dial and pointer of the meter are obtained by conducting Hough Transform to the meter image, and meter reading is achieved. Results of actual experiments with transformer substation indicate that, maximum of reading error is as low as 1.82% . The proposed method can obtain accurate and stable performance with multiple kinds of meters in complicated background, which meets the demand of practical application in transformer substation.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2017年第11期2813-2821,共9页
Chinese Journal of Scientific Instrument
关键词
指针式仪表
检测与识别
卷积神经网络
计算机视觉
pointer-type meter
detection and recognition
convolutional neural network
computer vision