Small Aircraft Detection in Remote Sensing Images Based on YOLOv3

Abstract

Accurate and rapid detection in remote sensing images is of great significance in military navigation, environmental monitoring, and civil applications. Due to the small object detection problem of remote sensing images, higher requirements and challenges are put forward for object detection technology. In recent years, the one-stage object detector and the two-stage object detector based on convolutional neural network have made great achievements in the field of image classification and detection. The one-stage object detector is generally superior to the two-stage object detector in detection speed, but the detection accuracy is inferior to the two-stage target detector. In this paper, YOLOv3 is used as a one- stage target detector for small aircraft detection of remote sensing images. We chose appropriate anchors to cover the size distribution in our experimental data by using dimension clusters. The experimental results show that YOLOv3 not only exceeds the conventional one-stage object detector in speed, but also well match the accuracy of the two-stage object detector. The YOLOv3 achieved excellent detection accuracy and low processing time at the same time for small aircraft detection.