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

摘要：

针对遥感图像中飞机目标检测精度低、检测速度慢、背景复杂等问题，提出了一种基于深度学习的改进YOLOv4目标检测算法。改进YOLOv4的主干特征提取网络，保留高分辨率的特征层，去除了用于检测大目标的特征层，减少语义丢失。在卷积神经网络中使用DenseNet（密集连接网络）加强对飞机目标的特征提取，减少梯度消失问题。对数据集使用[K]-means算法得到效果最佳的先验框数量和尺寸。在RSOD（Remote Sensing Object Detection）数据集和DIOR（Detection In Optical Remote sensing images）数据集上的实验表明，该算法满足实时性的需求，且该算法的精确度达到95.4%，较原算法提升了0.3个百分点；召回率达到86.04%，提升了4.68个百分点；mAP值达到85.52%，提升了5.27个百分点。

关键词: 遥感图像, 飞机目标, 卷积神经网络, YOLOv4