基于XMB-YOLOv5s的无人机小目标检测  

UAV Small Target Detection Based on XMB-YOLOv5s

作　　者：庄瑜 傅晓锦[1] 李莎 吴峥 ZHUANG Yu;FU Xiaojin;LI Sha;WU Zheng(College of Mechanical,Shanghai Dianji University,Shanghai 201306,China)

机构地区：[1]上海电机学院机械学院,上海201306

出　　处：《计算机与现代化》2025年第4期29-35,41,共8页Computer and Modernization

基　　金：上海市自然科学基金资助项目(11ZR1413800)。

摘　　要：无人机视角下密集小目标检测存在精度低、对部分目标误检、漏检等诸多不足。针对以上问题,本文提出一种基于XMB-YOLOv5s的无人机小目标检测技术。首先,采用小目标检测层,更加有效地提取和利用图像中的细节信息;其次,用BottleneckCSP和C3TR模块的结构化嵌入实现C3模块的更新,降低了运算复杂度,提高了训练效率;再次,融入CBAM注意力机制使网络能够更好地识别和处理特征,提升图像识别准确率;最后,采用Focal-EIoU Loss解决CIoU Loss对小目标检测不敏感的问题。实验结果表明:与传统的YOLOv5s算法相比,XMB-YOLOv5s算法在VisDrone2019数据集上P提高了4.6个百分点,R提高了4.4个百分点,mAP50提高了4.9个百分点,mAP75提高了5.1个百分点,mAP50-95提高了4个百分点,为无人机小目标检测提供了一种新的方法。From the drone viewpoint,the detection of dense,small targets faces various shortcomings,such as low accuracy,false detection of certain targets,and missed detections.To address these issues,this paper proposes a drone-based small target detection technique using XMB-YOLOv5s.Firstly,a small target detection layer is adopted for more effective extraction and utilization of detail information within the image.Secondly,the structured embedding of BottleneckCSP and C3TR modules is used to update the C3 module to reduce computational complexity and improve training efficiency.Subsequently,the integration of the CBAM attention mechanism enables the network to better recognize and process features,thus enhancing image recognition accuracy.Finally,the Focal-EIoU Loss is employed to resolve the insensitivity of the CIoU Loss to small target detection.The experimental results indicate that,compared with traditional YOLOv5s algorithm,the XMB-YOLOv5s algorithm has increased P by 4.6 percentage points,R by 4.4 percentage points,mAP50 by 4.9 percentage points,mAP75 by 5.1 percentage points,mAP50-95 by 4 percentage points on the VisDrone2019 data set,providing a novel approach for small target detection in drone applications.

关 键 词：无人机 深度学习 目标检测 机器视觉 XMB-YOLOv5s 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] TP399[自动化与计算机技术—计算机科学与技术]