基于轻量化YOLOv4机场场面遥感图像目标检测方法

Lightweight YOLOv4-based Target Detection Method for Remote Sensing Images of Airport Fields

针对现有遥感图像目标检测方法存在深层CNN丢失局部特征信息、复杂场景检测精度低的问题,提出一种基于轻量化YOLOv4的目标检测方法。首先,采用轻量级神经网络Ghostnet替代YOLOv4中作为主干特征提取的CSPdarknet53网络;其次,为改善复杂环境检测能力,采用CycleGAN仿真夜间场景;再次,融合Transformer模块,使模型易于采集网络特征间关系和局部信息;最后,采用Adam优化器和K-means++筛选锚框的方式加速收敛速度,并以RSOD航空遥感数据集进行实例验证。实验结果表明本文算法较原YOLOv4的MAP值提高了6.65个百分点,参数量减小了84.7%,可以满足复杂场景下的机场场面航空器实时目标检测。

Aiming at the problems that existing remote sensing image target detection methods suffer from the loss of local feature information in deep CNNs and low detection accuracy of complex scenes,a target detection method based on lightweight YOLOv4 is proposed.Firstly,the lightweight neural network Ghostnet is used to replace the cspdarknet53 network used as the backbone feature extraction in YOLOv4.Secondly,to improve the complex environment detection capability,CycleGAN is used to simulate night scenes,and again,the transformer module is fused to make the model easy to capture inter-feature relationships and local information of the network.Finally,Adam optimiser and K-means++screening anchor frame are used to accelerate the convergence speed,and the example is validated with RSOD aerial remote sensing dataset.The experimental results show that the MAP value is improved by 6.65 percentage points and the number of parameters is reduced by 84.7%compared with the original YOLOv4,i.e.the algorithm in this paper can meet the real-time target detection of aircraft on the airport field in complex scenes.