小区域引导下的水尺水位检测方法研究

Research on water gauge water level detection method under small area guidance

针对复杂环境下水尺识别及水位线检测存在准确率较低的问题,提出了一种小区域引导下的水尺水位检测方法.首先,利用改进的YOLOv4算法定位水尺上的“E”字符分割小区域;其次,在小区域内使用改进DeepLabv3+算法对水尺进行分割;再次,利用Canny算法提取水面边缘线;然后,通过统计像素坐标计算得到水位线;最后,使用线性插值法换算出水位值.实验结果表明,改进的YOLOv4的准确率均值为94.13%,高于其他目标检测网络;改进的DeepLabv3+的平均交并比为82.17%,高于其他分割网络;小区域引导下的水位线平均像素为3.030,低于直接分割,与人工读数进行对比误差小于1 cm,满足水文检测规范要求.与传统的图像检测方法相比,测量准确度更高.

Aiming at the low accuracy of water gauge identification and water level detection in complex environment,a small area guided water gauge water level detection method is proposed.Firstly,the improved YOLOv4 algorithm is used to locate the"E"character on the water gauge to segment the small area.Secondly,the improved DeepLabv3+algorithm is used to segment the water gauge in a small area.Thirdly,Canny algorithm is used to extract the water surface edge line.Then,the water level is calculated by statistical pixel coordinates.Finally,the water level is calculated by linear interpolation.The experimental results show that the mean pixel accuracy of the improved yolov4 is 94.13%,which is higher than that of other target detection networks.The mean intersection over Union of the improved DeepLabv3+is 82.17%,which is higher than that of other segmentation networks.The average pixel of the water level line under the guidance of a small area is 3.030,which is lower than the direct segmentation.The comparison error with the manual reading is less than 1 cm,which meets the requirements of hydrological detection specifications.Compared with traditional image detection methods,the measurement accuracy is higher.