基于改进YOLOv4的遥感图像目标检测

Remote sensing image target detection based on improved YOLOv4

针对遥感图像在复杂背景下因特征提取和表达能力不足而存在漏检和检测效果不佳的问题,提出一种优化特征提取网络的YOLOv4算法模型。该改进模型引入了一种新的Dense-PANet结构以获取更高的分辨率特征,并通过在特征提取网络中嵌入注意力机制以适应遥感图像因视野范围大而导致复杂背景下小目标漏检和检测效果不佳的问题。为了证明本文所提方法的有效性,针对DIOR遥感数据源进行了对比实验,结果表明,本文算法平均准确率(mean average precision,mAP)为86.55%,相比原算法提高了2.52%,较YOLOv3、RetinaNet提高了6.58%、14.09%,验证了所改进算法的有效性。

Aiming at the problem of missed detection and poor detection effect of remote sensing images due to insufficient feature extraction and expression capabilities in complex backgrounds,a YOLOv4 algorithm model that optimizes feature extraction network is proposed.The improved model introduces a new Dense-PANet structure to obtain higher resolution features,and embeds the attention mechanism in the feature extraction network to adapt to remote sensing images due to the large field of view,which leads to the missed detection of small targets in complex backgrounds and the problem of poor detection results.In order to prove the effectiveness of the method proposed in this paper,a comparative experiment was conducted on DIOR remote sensing data sources.The results show that the average accuracy(mean average precision,mAP)of the algorithm in this paper is 86.55%,which is an increase of 2.52%compared to the original algorithm.YOLOv3 and RetinaNet increased by 6.58%and 14.09%,which verifying the effectiveness of the improved algorithm.