基于改进YOLOv5s的无人机图像实时目标检测  

Real-time object detection for UAV images based on improved YOLOv5s

作　　者：陈旭 彭冬亮[1] 谷雨[1] Chen Xu;Peng Dongliang;Gu Yu(School of Automation,Hangzhou Dianzi University,Hangzhou,Zhejiang 310018,China)

机构地区：[1]杭州电子科技大学自动化学院,浙江杭州310018

出　　处：《光电工程》2022年第3期67-79,共13页Opto-Electronic Engineering

基　　金：浙江省自然科学基金资助项目(LY21F030010)。

摘　　要：针对无人机图像背景复杂、分辨率高、目标尺度差异大等特点,提出了一种实时目标检测算法YOLOv5sm+。首先,分析了网络宽度和深度对无人机图像检测性能的影响,通过引入可增大感受野的残差空洞卷积模块来提高空间特征的利用率,基于YOLOv5s设计了一种改进的浅层网络YOLOv5sm,以提高无人机图像的检测精度。然后,设计了一种特征融合模块SCAM,通过局部特征自监督的方式提高细节信息利用率,通过多尺度特征有效融合提高了中大目标的分类精度。最后,设计了目标位置回归与分类解耦的检测头结构,进一步提高了分类精度。采用VisDrone无人机航拍数据集实验结果表明,提出的YOLOv5sm+模型对验证集测试时交并比为0.5时的平均精度均值(mAP50)达到了60.6%,相比于YOLOv5s模型mAP50提高了4.8%,超过YOLOv5m模型的精度,同时推理速度也有提升。通过在DIOR遥感数据集上的迁移实验也验证了改进模型的有效性。提出的改进模型具有虚警率低、重叠目标识别率高的特点,适合于无人机图像的目标检测任务。As unmanned aerial vehicle(UAV)image has the characteristics of complex background,high resolution,and large scale differences between targets,a real-time detection algorithm named as YOLOv5sm+is proposed in this paper.First,the influence of network width and depth on UAV image detection performance was analyzed,and an improved shallow network based on YOLOv5s,which is named as YOLOv5sm,was proposed to improve the detection accuracy of major targets in UAV image through improving the utilization of spatial features extracted by residual dilated convolution module that could increase the receptive field.Then,a feature fusion module SCAM was designed,which could improve the utilization of detailed information by local feature self-supervision and could improve classification accuracy of medium and large targets through effective feature fusion.Finally,a detection head structure consisting with decoupled regression and classification head was proposed to further improve the classification accuracy.The experimental results on VisDrone dataset show that when intersection over union equals 0.5 mean average precision(mAP50)of the proposed YOLOv5sm+model reaches 60.6%.Compared with YOLOv5s model,mAP50 of YOLOv5sm+has increased 4.1%.In addition,YOLOv5sm+has higher detection speed.The migration experiment on the DIOR remote sensing dataset also verified the effectiveness of the proposed model.The improved model has the characteristics of low false alarm rate and high recognition rate under overlapping conditions,and is suitable for the object detection task of UAV images.

关 键 词：无人机图像 实时目标检测 YOLOv5sm+ 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]