Regional object detection of remote sensing airport based on improved deep neural network

The detection of multiple types of targets in the airport area under the satellite remote sensing monitor is of great military and civilian significance in real life. In order to effectively improve the detection accuracy of remote sensing images in the airport area, based on the representative deep network Faster R-CNN in the mainstream target detection method, the ReMD data enhancement algorithm is proposed for the data side. The deep ResNet network and the feature fusion component-FPN are used to extract more robust deep distinguishing features of airport area target. Finally, a new fully connected layer is added to the end detection network, and the softmax classifier and 4 logistic regression classifiers are combined to accurately classify airport area multi-class targets according to the target class correlation. Experiments show that the improvement of the original network brings a 11.6% increase in the average detection accuracy rate of the original network, reaching 80.5% mAP. Compared with other mainstream networks, it also has a better accuracy rate. At the same time, by appropriately reducing the input amount of the recommended area, under the premise of 3.2% reduction of accuracy rate, the detection time of 0.512 s is improved by 3 times to 0.173 s. According to the specific task, the accuracy and detection speed can be reasonably weighed, which reflects the effectiveness and practicability of the network. © 2021, Editorial Board of JBUAA. All right reserved.