Version 1
: Received: 30 August 2024 / Approved: 10 September 2024 / Online: 11 September 2024 (10:23:23 CEST)
How to cite:
Yang, Y.; Li, I.; Sang, N.; Liu, L.; Tang, X.; Tian, Q. Research on Large Scene Adaptive Feature Extraction Based on Deep Learning. Preprints2024, 2024090841. https://doi.org/10.20944/preprints202409.0841.v1
Yang, Y.; Li, I.; Sang, N.; Liu, L.; Tang, X.; Tian, Q. Research on Large Scene Adaptive Feature Extraction Based on Deep Learning. Preprints 2024, 2024090841. https://doi.org/10.20944/preprints202409.0841.v1
Yang, Y.; Li, I.; Sang, N.; Liu, L.; Tang, X.; Tian, Q. Research on Large Scene Adaptive Feature Extraction Based on Deep Learning. Preprints2024, 2024090841. https://doi.org/10.20944/preprints202409.0841.v1
APA Style
Yang, Y., Li, I., Sang, N., Liu, L., Tang, X., & Tian, Q. (2024). Research on Large Scene Adaptive Feature Extraction Based on Deep Learning. Preprints. https://doi.org/10.20944/preprints202409.0841.v1
Chicago/Turabian Style
Yang, Y., Xirui Tang and Qiyuan Tian. 2024 "Research on Large Scene Adaptive Feature Extraction Based on Deep Learning" Preprints. https://doi.org/10.20944/preprints202409.0841.v1
Abstract
The proliferation of intelligent monitoring devices has led to the widespread adoption of background extraction technology across a multitude of domains, including intelligent transportation, video surveillance, human-computer interaction, and medical diagnosis. In this work, the model employs a multi-layer convolutional neural network structure, which enables the extraction and fusion of scene features from different scales in a layer-by-layer manner. This approach facilitates the comprehensive capture of complex scene information. The convolutional network comprises multiple layers, with each layer responsible for extracting features at a specific scale. The shallower layers capture more detailed feature information, whereas the deeper layers focus on more global scene features. The strategy of multi-scale feature fusion allows the model to fully capture multi-level and multi-dimensional information in the context of large-scale scenes. Furthermore, the incorporation of an attention mechanism enables the model to adaptively allocate attention to salient regions, thereby enhancing its capacity to discern intricate scenes by assigning greater significance to pivotal features. The experimental results demonstrate that the proposed method is effective in practice, as evidenced by its performance on public datasets.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
