Research on Fast Detection Method of Remote Sensing Image Buildings Based on Hollow Convolution Model
Based on an end-to-end detection framework, this paper uses a multi-dilation rate convolution kernel group as a feature ex-traction module, and sets up dense connections between different feature extraction layers to enhance the information complexity in fea-ture maps of different scales; Based on multi-scale feature map fusion, the feature map upsampling pyramid of four output layers is constructed, and finally the expression ability of the target in the training set is improved through data augmentation. The test results show that the method in this paper can achieve high detection accuracy and real-time detection ability on the test data set, and has good generalization performance for multi-angle house targets in different backgrounds. This method has high practical value in the fields of urban illegal building supervision and smart city construction.
万方数据
维普
