Digital recognition method of methane sensor based on improved CNN-SVM

The methane sensor material has light reflection, and there are attachments on the display panel, which causes the poor quality of the sensor numerical image collected by the automatic verification system of methane sensor, and the difficulty of character recognition. However, the existing instrument character recognition methods based on machine learning have low recognition rate and slow algorithm running speed. In order to solve the above problems, a digital recognition method of methane sensor based on improved convolutional neural network (CNN) and support vector machine (SVM) is proposed. The numerical image of methane sensor is preprocessed by four steps, including image enhancement, numerical region image extraction, image segmentation and decimal point positioning. And the processed digital images are taken as a custom data set. In order to solve the problem of long running time of the CNN-SVM model, PCA algorithm is used to reduce the dimension of the image characteristics extracted from the CNN fully connected layer, and the most important data characteristics are used to replace the original data as the samples of the SVM classifier for classification and recognition. The verification results on the custom dataset show that the improved CNN-SVM model has higher accuracy and shorter running time than the traditional CNN model and CNN-SVM model. The verification results on the classical MNIST dataset show that considering the precision and real-time requirements, the improved CNN-SVM model has better comprehensive performance than CRNN, SSD, YOLOv3 and Faster R-CNN. A micro high-definition USB camera is used to collect the numerical images of methane sensor. The trained improved CNN-SVM model is transplanted to raspberry pi for image processing and recognition. The results show that the recognition success rate of methane sensor digital recognition method based on improved CNN-SVM is 99%, which is consistent with the simulation analysis results.