面向港口车船目标的航空影像旋转不变检测器
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摘要
通用目标检测器面向航空影像中地面目标显示出漏检和错检较多的缺陷。为了实现在航空影像下港口场景中的车辆和船只的位置检测并估计目标方向。一种单卷积旋转不变检测器被提出以解决这种问题。基于回归网络的旋转不变检测器网络层数更少,边框分配和尺度大小更加公平,在带有角度信息的方向边框策略的配合下能够在特定场景的目标检测任务中实现更鲁棒的检测结果并能评估目标的方向。使用同一数据集与众多先进架构的对比实验和旋转边框消融实验的结果证明了提出架构的优势。
The general purpose target detection architecture is insufficient in the vehicle and ship detection missions in the port scene under aerial imagery and cannot estimate the target direction. A rotation invariant detector has been proposed to solve this problem. Rotation-invariant detector network based on regression network has fewer layers,and border allocation and scale are more fair. In combination with the direction frame strategy with angle information,it can achieve more robust detection results and evaluate the direction of objects in target detection tasks in specific scenes. The results of the comparative experiments with the advanced architecture and the ablation experiments demonstrate the advantages of the proposed architecture.
The general purpose target detection architecture is insufficient in the vehicle and ship detection missions in the port scene under aerial imagery and cannot estimate the target direction. A rotation invariant detector has been proposed to solve this problem. Rotation-invariant detector network based on regression network has fewer layers,and border allocation and scale are more fair. In combination with the direction frame strategy with angle information,it can achieve more robust detection results and evaluate the direction of objects in target detection tasks in specific scenes. The results of the comparative experiments with the advanced architecture and the ablation experiments demonstrate the advantages of the proposed architecture.
引文
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2 Ning X,Li W,Tang B,et al. BULDP:biomimetic uncorrelated locality discriminant projection for feature extraction in face recognition[J]. IEEE Transactions on Image Processing,2018,27(5):1
3 Razakarivony S,Jurie F. Vehicle detection in aerial imagery:A small target detection benchmark[J]. Journal of Visual Communication and Image Representation,2015,34:187-203
4 Liu K,Mattyus G. Fast multiclass vehicle detection on aerial images[J]. IEEE Geoscience&Remote Sensing Letters,2015,12(9):1938-1942
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6 Sakla W,Konjevod G,mundhenk T N. Deep multi-modal vehicle detection in aerial ISR imagery[C]//Applications of Computer Vision. Santa Rosa:IEEE,2017:916-923
7 Ren S,He K,Girshick R,et al. Faster R-CNN:Towards real-time object detection with region proposal networks[J]. IEEE Trans Pattern Anal Mach Intell,2015,39(6):1137-1149
8 陈文康.基于深度学习的农村建筑物遥感影像检测[J].测绘,2016,39(5):227-230Chen Wenkang. Remote sensing image detection of rural buildings based on deep learning algorithm[J]. Surveying and Mapping of Sichuan,2016,39(5):227-230
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10 Redmon J,Divvala S,Girshick R,et al. You only look once:Unified,real-time object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas:IEEE Computer Society,2016:779-788
11 Lin T Y,Dollar P,Girshick R,et al. Feature pyramid networks for object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition. Venice:IEEE Computer Society, 2017:936-944
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14 Fu C Y,Liu W,Ranga A,et al. DSSD:deconvolutional single shot detector[A/OL].(2017-01-23)[2018-12-05]. https://arxiv.org/abs/1701. 06659
15 He K,Gkioxari G,Dollár P,et al. Mask R-CNN[C]//IEEE International Conference on Computer Vision. Venice:IEEE Computer Society,2017:2980-2988
16 Jia Y,Shelhamer E,Donahue J,et al. Caffe:Convolutional architecture for fast feature embedding[C]//Acm International Conference on Multimedia. Glasgow:ACM,2014:675-678
17 Deng J,Dong W,Socher R,et al. Image Net:A large-scale hierarchical image database[C]//Computer Vision and Pattern Recognition,2009. Miami:IEEE,2009:248-255
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