<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The computer vision direction in the field of artificial intelligence analyses the latest progress of computer vision technology from visual perception and visual generation, including but not limited to image recognition, target detection and image segmentation. First of all, for computer vision technology, this paper introduces the detailed application of image recognition technology, object detection technology and image segmentation technology. Then, we build a BP neural network combined with a deep LSTM neural network, use the BP network algorithm to select the input variables to reduce the dimension and complexity of the model, and use the selected variables as the input of the deep LSTM network. At the same time, deep LSTM is used to perform high-dimensional deep memory learning features on the selected variables. Finally, the model is separately experimented in computer vision. The experimental results show that the present model and other single models can be selected by BP neural network variables in computer vision applications, which can effectively reduce the complexity of the model and improve the generalisation ability of the model, so that it can be used in computer vision research.</p>
</abstract>

The computer vision direction in the field of artificial intelligence analyses the latest progress of computer vision technology from visual perception and visual generation, including but not limited to image recognition, target detection and image segmentation. First of all, for computer vision technology, this paper introduces the detailed application of image recognition technology, object detection technology and image segmentation technology. Then, we build a BP neural network combined with a deep LSTM neural network, use the BP network algorithm to select the input variables to reduce the dimension and complexity of the model, and use the selected variables as the input of the deep LSTM network. At the same time, deep LSTM is used to perform high-dimensional deep memory learning features on the selected variables. Finally, the model is separately experimented in computer vision. The experimental results show that the present model and other single models can be selected by BP neural network variables in computer vision applications, which can effectively reduce the complexity of the model and improve the generalisation ability of the model, so that it can be used in computer vision research.

Research on computer vision technology based on BP-LSTM hybrid network

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Research on computer vision technology based on BP-LSTM hybrid network"}

The computer vision direction in the field of artificial intelligence analyses the latest progress of computer vision technology from visual perception and...

Research on computer vision technology based on BP-LSTM hybrid network

Online veröffentlicht: 10. Apr. 2023

Seitenbereich: 975 - 984

Eingereicht: 15. Juni 2022

Akzeptiert: 07. Aug. 2022

DOI: https://doi.org/10.2478/amns.2021.2.00270

Schlüsselwörter
deep neural network, BP network, LSTM, computer vision

© 2023 Qiaoling Yi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on computer vision technology based on BP-LSTM hybrid network

Online veröffentlicht: 10. Apr. 2023

Seitenbereich: 975 - 984

Eingereicht: 15. Juni 2022

Akzeptiert: 07. Aug. 2022

DOI: https://doi.org/10.2478/amns.2021.2.00270

Schlüsselwörterdeep neural network, BP network, LSTM, computer vision

© 2023 Qiaoling Yi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
deep neural network, BP network, LSTM, computer vision