Building Recognition Based on Improved Faster R-CNN in High Point Monitoring Image

In this study, the Faster R-CNN model is trained to recognize the building based on the monitoring video image data captured by the high-resolution camera (high point) of the 40 meter communication tower. Aiming at the problem that the building target in the foreground is small and the detection effect is not good, the Faster R-CNN network structure is improved, and the low-level features and high-level features are used to detect the target in different scales. The experimental results show that the average accuracy of this method is 75.58%, which can effectively detect buildings. It can be applied to the law enforcement and inspection of illegal occupation of land, improve the work efficiency of government departments, and reduce the cost of manual inspection.