The Research On The Detection Technology Of Water Level Ruler Based On Image

Water level data is important information for dealing with flood disasters,flood control command and water conservancy project scheduling.Due to the characteristics of the measuring device,the traditional water level detection method is greatly affected by environmental factors,is not easy to install,and has a high maintenance cost and failure rate in the later period.With the development of computer vision and digital image processing technology,automatic water level recognition can be realized based on video image data.This method effectively solves the problems of traditional water level detection.However,the current water level intelligent video recognition technology has technical problems that are susceptible to low-illuminance environments and low accuracy,which limits its further promotion and application.In view of the above problems,this thesis conducts in-depth research on the image-based water level gauge method,proposes corresponding solutions,and obtains good research results.The main research contents of this thesis are as follows:(1)The water level image data collected under low light conditions at night has problems that it is difficult to correctly extract the water level line and read the water gauge.The traditional low-light image enhancement algorithm has the enhanced image area overexposure,color distortion,and serious noise points.Shortcomings.In response to these problems,this thesis proposes an improved algorithm for low-light image enhancement based on deep learning,RKin D.By improving the network structure of the layer decomposition module of the Kin D algorithm,introducing a batch normalization layer,adding an image fusion module and a feature fusion module,it solves the problem that the Kin D network is relatively shallow,the structure is simple,and the image feature extraction is not rich enough.The experiment verifies that while ensuring the image enhancement,the noise amplification is reduced and the image blur is reduced.This method effectively improves the brightness of the water gauge image and maintains better clarity and contrast,providing good image data for subsequent water level detection.(2)In order to realize the automatic recognition of the water level scale based on the image,this thesis proposes an effective water level detection method.This method first converts the water gauge image to HSV color space,evaluates the brightness of the image,eliminates noise through median filtering and morphological operations,and then extracts the water gauge area by calculating the minimum enclosing matrix.Use Hough transform to correct the tilt of the water gauge image,then use character normalization and build a convolutional neural network to identify the water gauge numbers through character features;then use the connected domain labeling algorithm to calculate the number of complete tick marks;finally The water level situation gives the corresponding water level calculation method.The above method can meet the accuracy requirements of water level measurement through experimental verification and achieve the expected goal.(3)Combining the above-mentioned low-light image enhancement algorithm RKin D and image-based water level detection method,this thesis designs and implements a water level detection system,which is divided into a front-end water level detection and analysis system and a back-end water level detection information WEB system.There are5 functional modules.The former includes a video stream processing module,a water level image processing module,and a water level data calculation module;the latter includes a water level data statistical analysis module and a water level early warning push module.The results of the system operation show that the image-based water level gauge detection system in this thesis has reached the design requirements and has a high practical and popularization value.