Target Detection Algorithm Using Air-to-Ground Scene Information

被引：0

作者：

Zhao T. ^{[1
]}

Liu J. ^{[1
]}

Liu X. ^{[1
]}

机构：

[1] Missile Engineering College, Rocket Force University of Engineering, Xi'an

来源：

Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2019年 / 31卷 / 10期

关键词：

Air-to-ground perspective; Object detection; Scene auxiliary; Single shot multibox detector;

D O I：

10.3724/SP.J.1089.2019.17680

中图分类号：

学科分类号：

摘要：

The scene information of image is more abundant in the air-to-ground perspective, which is helpful for the loca-tion and classification of target. The traditional single shot multibox detector (SSD) net predicts bounding boxes and classifications of the target from six feature maps, ignoring the auxiliary effect of high-level semantic fea-ture maps on the ﬁne details of shallow layers. In order to combine scene information, firstly, the effects of different scale feature maps on target detection are analyzed on the basis of SSD net. And then a new scene auxiliary structure is established by combining the ideas of feature pyramid networks and long short term memory net to enhance representation of feature maps. At last, the method is validated on a self-made dataset of air-to-ground and compared with several classic networks in the detection domain. The results show that the proposed method has higher detection accuracy than others, which can detect targets efficiently under the prerequisite of guaranteeing speed. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.

引用

页码：1795 / 1801

页数：6

共 16 条

[1] Cai Z.W., Vasconcelos N., Cascade R-CNN: delving into high quality object detection
[2] Ren S.Q., He K.M., Girshick R., Et al., Faster R-CNN: towards real-time object detection with region proposal networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 6, pp. 1137-1149, (2017)
[3] Lin T.Y., Goyal P., Girshick R., Et al., Focal loss for dense object detection, Proceedings of the IEEE International on Computer Vision, pp. 2999-3007, (2017)
[4] Liu W., Anguelov D., Erhan D., Et al., SSD: single shot multibox detector, Proceedings of European Conference on Computer Vision, pp. 21-37, (2016)
[5] Fu C.Y., Liu W., Ranga A., Et al., DSSD: deconvolutional single shot detector
[6] Lin T.Y., Dollar P., Girshick R., Et al., Feature pyramid networks for object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 936-944, (2017)
[7] Li Z.X., Zhou F.Q., FSSD: feature fusion single shot multibox detector
[8] Xu M.L., Cui L.S., Lv P., Et al., MDSSD: multi-scale deconvolutional single shot detector for small objects
[9] Zhao Q.J., Sheng T., Wang Y.T., Et al., CFENet: an accurate and efficient single-shot object detector for autonomous driving
[10] Leng J.X., Liu Y., An enhanced SSD with feature fusion and visual reasoning for object detection, Neural Computing and Applications, pp. 1-10, (2018)

← 1 2 →