关键词: 遥感图像, 飞机目标, 卷积神经网络, YOLOv4

Abstract:

Aiming at the problems of low accuracy, slow detection speed and complex background of aircraft targets in remote sensing images, an improved YOLOv4 target detection algorithm based on deep learning is proposed. The backbone feature extraction network of YOLOv4 is improved to retain the high-resolution feature layer, remove the feature layer used to detect large targets, and to reduce semantic loss. DenseNet （Densely connected Network） is adopted to enhance feature extraction and reduce the vanishing gradient problem. The [K]-means algorithm on the data set is used to get the best prior frame number and size. Experimental results on RSOD（Remote Sensing Object Detection） data set and DIOR（Detection in Optical Remote sensing images） data set show that the accuracy of the proposed algorithm reaches 95.4%, which is 0.3 percentage points higher than original algorithm, and the recall rate reaches 86.04%, an increase of 4.68 percentage points, and then mAP value reaches 85.52%, an increase of 5.27 percentage points.

Key words: remote sensing, aircraft target, convolutional neural network, YOLOv4