基于灰度拉伸的图像水位识别方法研究

Study on image water level recognition based on grayscale stretching

图像法水位测量通过图像处理技术检测水位线,实现水位信息的自动获取。然而,由于现场环境光照条件复杂、清水倒影、成像分辨率低和视角倾斜的影响,水尺表面字符和刻度线的识别相当不可靠。为提高复杂光照条件下水位值的精度,本文设计了一种基于灰度拉伸的水位线检测方法。首先,构造一种新的结合卷积神经网络(CNN)和残差的去噪模型,在去除水尺图片噪声的同时能够较好地保持水尺的细节。然后,通过灰度直方图统计水面、背景、水尺部分的灰度值并进行分析,确定灰度拉伸的范围,明确水尺与水体部分的分界线来定位水位线。

The image method of water level measurement detects the water level line through image processing technology to realize the automatic acquisition of water level information.However,due to the effects of complex on-site ambient lighting conditions,clear water reflection,low imaging resolution,and oblique viewing angles,the recognition of characters and tick marks on the water gauge surface is quite unreliable.In order to improve the accuracy of the water level value under complex lighting conditions,a detection method of the water level is designed based on grayscale stretching.First of all,a new denoising model is constructed based on residual learning and convolutional neural network(CNN),which can maintain the details of the water gauge while removing the noise of the water gauge image.And then,the grayscale histogram is used to count the grayscale values of the water surface,background,and water gauge,and analyze to determine the range of gray stretch,and clarify the boundary between the water gauge and the water body to locate the water level.