Image Classification and Object Detection Algorithm Based on Convolutional Neural Network
Journal Title: Science Insights - Year 2019, Vol 31, Issue 1
Abstract
Traditional image classification methods are difficult to process huge image data and cannot meet people’s requirements for image classification accuracy and speed. Convolutional neural networks have achieved a series of breakthrough research results in image classification, object detection, and image semantic segmentation. This method broke through the bottleneck of traditional image classification methods and became the mainstream algorithm for image classification. Its powerful feature learning and classification capabilities have attracted widespread attention. How to effectively use convolutional neural networks to classify images have become research hotspots. In this paper, after a systematic study of convolutional neural networks and an in-depth study of the application of convolutional neural networks in image processing, the mainstream structural models, advantages and disadvantages, time / space used in image classification based on convolutional neural networks are given. Complexity, problems that may be encountered during model training, and corresponding solutions. At the same time, the generative adversarial network and capsule network based on the deep learning-based image classification extension model are also introduced; simulation experiments verify the image classification In terms of accuracy, the image classification method based on convolutional neural networks is superior to traditional image classification methods. At the same time, the performance differences between the currently popular convolutional neural network models are comprehensively compared and the advantages and disadvantages of various models are further verified. Experiments and analysis of overfitting problem, data set construction method, generative adversarial network and capsule network performance.
Authors and Affiliations
Juan K. Leonard
Keywords
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- EP ID EP689663
- DOI 10.15354/si.19.re117
- Views 187
- Downloads 0
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