Real-time Detection of Aircraft Objects in Remote Sensing Images Based on Improved YOLOv4

In recent years, the application of object detection in the military field has become more and more extensive, and the detection of aircraft objects in remote sensing images can provide data support for accurate object strikes. In this paper, we propose an real-time aircraft object detection method in remote sensing images based on YOLOv4 object detection algorithm. We improved the YOLOv4 object detection algorithm by replacing the traditional convolution in Res_unit with a depthwise separable convolution, replacing the Mish activation function in the backbone with the ELU activation function, and adding an SE module in each CSP_unit. The obtained algorithm was named Aircraft-YOLOv4 finally. The mAP and fps of Aircraft-YOLOv4 when detecting aircraft objects in remote sensing images can reach 86.92% and 29.62, respectively, realizing real-time detection, which is 2.82% and 7.01 higher than YOLOv4. And Aircraft-YOLOv4 has improved performance in all aspects when model is tested on UCAS-AOD, a dataset similar to the RSOD-Dateset used for training. The experimental results show that Aircraft-YOLOv4 has good generalization and is more suitable for aircraft object detection tasks in remote sensing images in the military field than YOLOv4.