基于特征增强YOLOv4的无人机检测算法研究

Research on UAV detection method based on feature enhanced YOLOv4 algorithm

现有基于深度学习的目标检测方法在面对空中消费级无人机时,存在鲁棒性差、准确率不足等问题。对此,提出一种基于特征增强的YOLOv4目标检测方法—FEM-YOLOv4。首先,针对无人机低、小、慢等特点,改进骨干网络,降低下采样倍数,充分利用包含细粒度信息的浅层特征;其次,加入特征增强模块(feature enhancement module),通过使用不同空洞率的多分支卷积层结构,综合不同深度的语义信息和空间信息,增强小尺度无人机的细节语义特征;另外,利用多尺度融合的特征金字塔结构,突出特征图包含的细节信息和语义信息,提升模型对无人机目标的预测能力;最后,采用K-means++算法对无人机目标候选框的尺寸进行聚类分析。与6种目标检算法进行对比,实验结果表明,FEM-YOLOv4算法的mAP和Recall分别达到89.48%、97.4%,优于其他算法,且平均检测速度为0.042 s。

Consumer-level UAVs have small scale,low fly speed and height,existing deep learning methods hardly achieve high detection accuracy and good robustness on detecting UAVs.In order to address this problem,this paper develops an improved YOLOv4 algorithm with feature enhanced module named as FEM-YOLOv4 for UAVs detection.Firstly,according to the characteristics of UAVs,this paper reduces the subsampling multiple of CSPDarkNet to improve the backbone network and make full use of shallow features containing detailed information.Secondly,this paper introduces the feature enhancement module to replace the SPP module.The feature enhancement module includes multiple branches and dilated convolution,and it obtains different levels of semantic information,which is beneficial to enhance the detailed semantic features and the detection capabilities of the network.Thirdly,delete the PAN module to improve the feature pyramid,and compress the depth of each detection layer to highlight the detailed and semantic information of the feature maps.Finally,the anchor box is initialized by the K-means++algorithm to make the model more suitable for predicting the UAV targets.Compared with the six target detection algorithms,the experimental results show that the mAP and Recall of FEM-YOLOv4 algorithm reach 89.48%and 97.4%respectively,which are superior to other algorithms,and the average detection speed is 0.042 s.