南京航空航天大学学报（英文版）2021，Vol.38Issue(4)：571-586,16.
SA-FRCNN:一种改进的机场停机坪目标检测方法
SA-FRCNN:An Improved Object Detection Method for Airport Apron Scenes
摘要
Abstract
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 SA-FRCNN 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.关键词
机坪场景/目标检测/图卷积网络/空间上下文/注意力机制Key words
airport apron scene/object detection/graph convolutional network/spatial context/attention mechanism分类
信息技术与安全科学引用本文复制引用
吕宗磊,陈丽云..SA-FRCNN:一种改进的机场停机坪目标检测方法[J].南京航空航天大学学报（英文版）,2021,38(4):571-586,16.基金项目
This work was supported by the Fun-damental Research Funds for Central Universities of the Civ-il Aviation University of China(No.3122021088). （No.3122021088）
