基于多分支融合网络的遥感飞机检测算法

A Remote Sensing Aircraft Detection Algorithm Based on Multi-Branch Fusion Network

针对目前遥感图像检测精度低、召回率低、实时性差等问题,提出基于GhostNet和CoT多分支残差网络(MBRNet)的遥感飞机检测算法。借鉴YOLOv4网络模型,采用MBRNet作为新的主干网络,从而减少梯度消失问题并弥补了CNN欠缺的全局特征计算能力;为了减少小目标丢失问题,同时在主干与PANet中引入多方位的特征提取与融合思路,实现在高、低特征层之间和同尺度特征层之间的信息充分互补。提出的算法在具有背景复杂、过度曝光、目标密集等场景的RSOD和LEVIR数据集上准确率达到了97.64%,召回率达到了89.11%。

Aiming at the problems of low detection accuracy,low recall rate,and poor real-time performance of remote sensing images,a remote sensing aircraft detection algorithm based on GhostNet and CoT(Contextual Transformer) Multi-Branch Residual Network(MBRNet) is proposed. Learning from the YOLOv4 network model,MBRNet is adopted as new backbone network to reduce the problem of gradient disappearance and makes up for the lack of global feature calculation capabilities of CNN. In order to reduce the problem of small target loss,multi-directional feature extraction and fusion are introduced into the backbone and PANet. The idea is to realize full complementation of information between high and low feature layers and between feature layers of the same scale.The proposed algorithm has an accuracy of 97.64% and a recall rate of 89.11% on RSOD and LEVIR data sets in the circumstance of complex background,overexposure and dense targets.