Improvement of Detection Accuracy of Aircraft in Remote Sensing Images Based on YOLOV5 Model

Recently, object detection has experienced great progress. However, in the field of remote sensing, it is still under investigation for real-time detection of small objects because of the limited resolution and information. To detect a predefined object, such as aircraft, is important in some applications. This paper aims to improve the detection accuracy of the aircraft. Meanwhile, the speed of detection is another factor to be considered. At present, the model of You Only Look Once v5s (YOLOv5s) for object detection can achieve a good trade-off between accuracy and speed. So, YOLOv5s is adopted in the study. The model is trained with 600 remote-sensing images from UCAS-AOD dataset and tested with another 400 images from the same dataset. The experimental results show that YOLOv5 achieves 99.3% AP
50
in 74 seconds. It proves this model can effectively detect aircrafts in real time and achieve high accuracy.