High-resolution remote sensing vehicle automatic detection based on feature fusion convolutional neural network

Xin Li; Kai Guo; Mutailifu Subei; Dudu Guo

doi:10.1117/12.2626645

20 December 2021 High-resolution remote sensing vehicle automatic detection based on feature fusion convolutional neural network

Xin Li, Kai Guo, Mutailifu Subei, Dudu Guo

Author Affiliations +

Proceedings Volume 12155, International Conference on Computer Vision, Application, and Design (CVAD 2021); 121550L (2021) https://doi.org/10.1117/12.2626645
Event: International Conference on Computer Vision, Application, and Design (CVAD 2021), 2021, Sanya, China

Abstract

Vehicle detection technology based on remote sensing images, as a new method of collecting traffic flow information, provides new ideas for traffic management. A feature-fusion-based convolutional neural network vehicle detection method is proposed. On the basis of image preprocessing, first use the VGG16 convolutional neural network to obtain multi-level features, and then use variable-scale stacking to obtain the basic feature layer to achieve the acquisition of deep convolution features, and then construct a feature pyramid to divide the basic feature layer operation, finally use the attention mechanism to fuse hierarchical information, and then efficiently extract vehicle features. In the example high-resolution remote sensing image vehicle automatic detection experiment, the vehicle automatic detection accuracy rate was 88.7%, and the false detection rate was 1.4%. The experiment shows that this model is better for automatic vehicle detection in high-resolution remote sensing images, especially in dense urban traffic scenes. good detection effect.

Citation Download Citation

Xin Li, Kai Guo, Mutailifu Subei, and Dudu Guo "High-resolution remote sensing vehicle automatic detection based on feature fusion convolutional neural network ", Proc. SPIE 12155, International Conference on Computer Vision, Application, and Design (CVAD 2021), 121550L (20 December 2021); https://doi.org/10.1117/12.2626645

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KEYWORDS

Remote sensing

Convolutional neural networks

Feature extraction

Roads

Convolution

Image fusion

Statistical modeling

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