Fig. 1. Flow chart of airport detection method combined with continuous learning of residual-based network

Fig. 2. Constructing airport object core set by binary tree method

Fig. 3. Airport object detection network structure combined with residual block network

Fig. 4. Improve the scale of anchor in RPN for airport object

Fig. 5. Visualization of the feature map of the target in the image by the residual network. (a) Original true color image; (b) feature map from 64 convolution kernels; (c) feature map from 1024 convolution kernels; (d) feature heat map obtained from classification regression and border regression

Fig. 6. Verification set loss function decline curve and accuracy test. (a) Loss value decline curve; (b) accuracy test curve

Fig. 7. Airport object detection results in remote sensing images under different background environments. (a) Test result in hilly area environment; (b) test result in desert environment; (c) test result in an island environment; (d) test result in port environment

Fig. 8. Misclassification caused by background texture similar to airport objects. (a) Bridge facilities; (b) industrial park; (c) highway; (d) structured experimental field

Fig. 9. Airport object detection results under interference conditions. (a) Cloud interference; (b) sweeping incomplete; (c) large difference in object scale in the same image; (d) small airport

Fig. 10. Comparison of airport detection results in continuous learning mode. (a) CLRNet 1st stage detection results under the condition that the target texture shape is similar to the airport; (b) CLRNet 2nd stage detection result under the condition that the target texture shape is similar to the airport; (c) CLRNet 1st stage detection result under background environment interference; (d) CLRNet 2nd stage detection result under background environment interference