Using Convolutional Neural Networks (CNN) to Realize Deep Recognition Analysis of Power Technology Standard Images
Shan Ting Liu, Jin Kao Zhao, Haiqi Wang, Rui Li, Zhumao Lu · Procedia Computer Science · 2024
This article mainly studies the method of deep recognition analysis of power technology standard images using Convolutional Neural Network (CNN). In this study, an efficient CNN model was constructed to quickly and accurately classify and recognize common technical standard images in the power industry. The experimental results indicate that the research method has high accuracy and good practicality in the recognition of power technology standard images. In the experimental stage, this study designed four experiments to explore the application of CNN models in power technology standard image recognition, with a focus on evaluating the impact of different network designs and data processing strategies on model performance. In the benchmark model comparison experiment, the AUC (Area Under the ROC Curve) value of the CNN model in the power technology standard image recognition task was 0.96, the AUC value of the Support Vector Machine (SVM) was 0.88, and the AUC value of the decision tree model was 0.82. In experiments on the impact of different data augmentation strategies, the CNN model using scaling augmentation strategy achieved an accuracy of 90%. In the experiment on the impact of network depth on performance, the accuracy of the CNN model was highest improved to 95%. In real-time recognition capability testing, although the CNN model has the highest accuracy, the average processing time for each image is also relatively long. In the above data conclusion, the effectiveness of the CNN model in power technology standard image recognition tasks has been demonstrated, and possible paths for optimizing model performance in practical applications have also been revealed.