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. 2022 Jun 29:2022:3464984.
doi: 10.1155/2022/3464984. eCollection 2022.

An Improved Convolutional Neural Network-Based Scene Image Recognition Method

Pinhe Wang  1 , Jianzhong Qiao  1 , Nannan Liu  2
Affiliations

An Improved Convolutional Neural Network-Based Scene Image Recognition Method

Pinhe Wang et al. Comput Intell Neurosci. .

Abstract

To solve the problems existing in the research of scene recognition, this paper studies a new convolutional neural network target detection model to achieve a better balance between the accuracy and speed of high-speed scene image recognition. First, aiming at the problem that the image is easy to be disturbed by impurities and poor quality in fine-grained image recognition, a preprocessing method based on the Canny edge detection is designed and the Canny operator is introduced to process the gray image. Second, the L2 regularization algorithm is used to optimize the basic network framework of the convolutional neural network, enhance the stability of the model in a complex environment, improve the generalization ability of the model, and improve the recognition accuracy of the algorithm to a certain extent. Finally, by collecting the campus environment datasets under different environmental conditions, the location recognition experiment and heat map visualization experiment are carried out. Experiments show that compared with the basic convolution neural network algorithm, the algorithm has better recognition performance and good generalization ability. The research of this study realizes the effective combination of multiframe convolution neural network and batch normalization algorithm and has a good practical effect on scene image recognition.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Simple structure of the STN network model.
Figure 2
Figure 2
Improved convolutional neural network algorithm model.
Figure 3
Figure 3
Improved convolutional neural network-based scene image recognition algorithm.
Figure 4
Figure 4
Effect of the number of filter cores of the improved convolutional neural network module.
Figure 5
Figure 5
Image recognition accuracy of each algorithm.
Figure 6
Figure 6
Accuracy-recall relationship curves.
Figure 7
Figure 7
Loss curve.
Figure 8
Figure 8
Classification accuracy of scene image recognition model for three iteration cycles.
Figure 9
Figure 9
Recognition rate results under different combinations.
See this image and copyright information in PMC

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