Research On And Applications Of The Embedded Image Identification And Information Collection System

A fast-paced advance in the computing and information technology has contributed to a huge improvement in image identification,with much of the attention being drawn to the collection and processing of meter image in the machinery-manufacturing sector.In respect to the identification of remote non-network chemical equipment and the collection of instrument data,we research the BP Neural Network Algorithm as well as the Support Vector Machine Model intended for image identification,both of which rely on an embedded image collection system to deliver internet-based image identification realize data acquisition.Firstly,we present a design for the overall solution through laying out the basic frameworks within which the image identification operates by virtue of the BP Neural Network Algorithm as well as the SVM model.By doing so,the solution to software design is identified for the embedded image identification system,after which a further analysis of the hardware platform used to run the system facilitates the set-up of a Linux software development environment.Secondly,we take advantage of the advanced learning technology that the BP Neural Network Algorithm has to deal with the problems of character identification while the image processing is ongoing.For the process of chemical controls,we also make an improvement to the characters to be shown on the temperature gauge by using vertical projection which allows an accurate identification through character division.This sort of algorithm is also verified for its validity.Thirdly,we do research on the SVM model for its classified identification technology and put forward an image classification method with the integration characteristics as its SVM input vectors.This article takes the image of a boiler as the subject for our research.We start by collecting the information about its color as well as the features in VTD,HTD,LBP and HOG and integrating these multiple features in a tandem way.Afterwards,we use all components,singular feature as well as the integrated features as separate input vectors for the SVM model.In order to verify the validity of fusion feature input,the input vectors of all components,single feature and fusion feature are respectively used as support vector machines,and the F Value of comprehensive evaluation index corresponding to different input vectors is obtained,and the results show that the input vectors of fusion features as support vector machines have good robustness.The most suitable fusion features are defined according to the combination of training time and the size of the classification model.Finally,according to our research results,we launch an embedded bottom layer-based image identification and information collecting system operating on the JZ2440 embedded development board as its hardware platform.It applies the BP Neural Network algorithm and SVM model to reach a good accuracy in the identification of meter image for chemical controls.It shows that the image recognition technology based on embedded system has important practical value in the field of industry information gathering.