SA-FRCNN:An Improved Object Detection Method for Airport Apron Scenes  

SA-FRCNN:一种改进的机场停机坪目标检测方法

作　　者：LYU Zonglei CHEN Liyun 吕宗磊;陈丽云(中国民航大学计算机科学与技术学院,天津300300;中国民航信息技术科研基地,天津300300)

机构地区：[1]College of Computer Science and Technology,Civil Aviation University of China,Tianjin 300300,P.R.China [2]Information Technology Research Base of Civil Aviation Administration of China,Tianjin 300300,P.R.China

出　　处：《Transactions of Nanjing University of Aeronautics and Astronautics》2021年第4期571-586,共16页南京航空航天大学学报（英文版）

基　　金：supported by the Fundamental Research Funds for Central Universities of the Civil Aviation University of China(No.3122021088).

摘　　要：The airport apron scene contains rich contextual information about the spatial position relationship.Traditional object detectors only considered visual appearance and ignored the contextual information.In addition,the detection accuracy of some categories in the apron dataset was low.Therefore,an improved object detection method using spatial-aware features in apron scenes called SA-FRCNN is presented.The method uses graph convolutional networks to capture the relative spatial relationship between objects in the apron scene,incorporating this spatial context into feature learning.Moreover,an attention mechanism is introduced into the feature extraction process,with the goal to focus on the spatial position and key features,and distance-IoU loss is used to achieve a more accurate regression.The experimental results show that the mean average precision of the apron object detection based on SAFRCNN can reach 95.75%,and the detection effect of some hard-to-detect categories has been significantly improved.The proposed method effectively improves the detection accuracy on the apron dataset,which has a leading advantage over other methods.机坪场景下包含丰富的空间位置关系上下文信息。传统目标检测器往往只关注单一的视觉外观而忽略上下文信息;此外机坪数据集中部分类别识别准确率较低。针对上述问题,提出一种改进的机场停机坪目标检测方法,称为SAFRCNN。该方法利用图卷积网络来捕获机坪场景下目标间的相对空间关系,将空间位置关系上下文融入模型生成空间感知特征;在特征提取过程中引入注意力机制,聚焦机坪目标的空间位置和关键特征;使用距离交并比损失实现目标更精确地回归定位。实验结果表明,SAFRCNN方法在机坪数据集上目标检测均值平均精准度达到95.75%,部分较难检测类别的检测效果提升显著;有效提高机坪目标检测的准确性,较其他方法具有领先优势。

关 键 词：airport apron scene object detection graph convolutional network spatial context attention mechanism 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]