Airport objects are hotspots in the field of image object detection because of their specific features and value for applications. In this study, we developed a complex object detection method based on improved Faster R-CNN to achieve higher detection precision to detect seven types of remote sensing image objects in airport areas under complex conditions such as different scales, different visual angles, and different backgrounds. When building the network, we used deeper basic networks and feature fusion components to extract more robust features. At the same time, we had also modified the selection of positive and negative samples to improve sample imbalance. The main improvements in the algorithm concern the anchor size generation rule, and the addition of an a priori judgment network for the network. The effectiveness of the improved algorithm was verified in experiments. Compared with the original Faster R-CNN, the improved network brings a 12.7% increase in mAP, at the detection time of 0.307s. Finally, the model with trained weights was used to test the detection of the seven types of objects in airport areas on different data sets, and comparisons were conducted with other algorithms. The experimental results showed that the method improved the average detection accuracy and had a good performance in remote sensing airport object detection tasks.
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