改进YOLOv3算法下通航机场场面运动目标检测  

Moving target detection of general aviation airport based on improved YOLOv3 algorithm

作　　者：夏正洪[1] 魏汝祥 李彦冬 XIA Zhenghong;WEI Ruxiang;LI Yandong(School of Air Traffic Management,Civil Aviation Flight University of China,Guanghan Sichuan 618307,China;School of Aviation Engineering,AnYang University,Anyang Henan 455000,China)

机构地区：[1]中国民用航空飞行学院空中交通管理学院,四川广汉618307 [2]安阳学院航空工程学院,河南安阳455000

出　　处：《中国安全科学学报》2023年第2期82-88,共7页China Safety Science Journal

基　　金：四川省科技计划项目(22ZDYF0196);中飞院智慧民航专项重点项目(ZHMH2022-002);中国民用航空飞行学院研究生科研创新基金资助(XSY2022-30)。

摘　　要：为获得更好的检测精度和更快的检测速度,保障通航机场场面运行安全,提出一种改进的YOLOv3算法,分别从网络结构和损失函数2方面进行改进。首先,在主干网络中使用深度可分离卷积代替原卷积,构建基于距离交并比(DIoU)的目标框回归损失函数;然后,以某通航机场为研究对象,搭建通航机场场面目标检测场景,采用迁移学习和冻结训练相结合的训练方法,以提升场面目标检测的速度;最后,比较分析所提算法与传统的YOLOv3、YOLOv4算法的识别效果。结果表明:飞机目标的检测效果明显优于车辆和人员目标,改进的YOLOv3算法对目标的检测精度、召回率、全类平均精度(mAP)分别达到92.96%、80.51%、91.96%,图形处理器处理速度高达74帧/s,较传统的YOLOv3、YOLOv4算法性能均有明显提升,可实现通航机场场面运动目标的有效检测。In order to obtain better detection accuracy and faster detection speed,and ensure the safety of airport surface operation,an improved YOLOv3 algorithm was proposed in this paper,which was improved from two aspects:network structure and loss function.Firstly,the depth-wise separable convolution was used to replace the original convolution in the backbone network,and then the regression loss function of the target frame based on DIoU ratio was constructed.Taking a general airport as the research object,a surface target detection scene was built,and a training method combining migration learning and freezing training was adopted to improve the speed of surface target detection.Finally,the recognition effect of the proposed algorithm was compared with that of the traditional YOLOv3 and YOLOv4 algorithms.The results show that the detection accuracy,recall and mean average precision(mAP)of the improved YOLOv3 algorithm are 92.96%,80.51%and 91.96%,respectively,and the graphics processing unit processing speed is 74 f/s.Compared with the traditional YOLOv3 algorithm and YOLOv4 algorithm,the performance of the improved YOLOv3 algorithm is significantly improved,which can realize the effective detection of moving targets and further ensure the operation safety of general aviation airports.

关 键 词：改进YOLOv3算法 通航机场 目标检测 深度可分离卷积 距离交并比(DIoU) 

分 类 号：X949[环境科学与工程—安全科学] V355[航空宇航科学与技术—人机与环境工程]