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.
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