Research on Bidirectional Attenuation Loss Method for Rotating Object Detection in Remote Sensing Image

ZHANG Zheng; MA Yubo; LIU Chang’an; TIAN Qing

doi:10.11999/JEIT220991

Volume 45 Issue 10

Oct. 2023

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ZHANG Zheng, MA Yubo, LIU Chang’an, TIAN Qing. Research on Bidirectional Attenuation Loss Method for Rotating Object Detection in Remote Sensing Image[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3578-3586. doi: 10.11999/JEIT220991

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ZHANG Zheng, MA Yubo, LIU Chang’an, TIAN Qing. Research on Bidirectional Attenuation Loss Method for Rotating Object Detection in Remote Sensing Image[J]. Journal of Electronics & Information Technology, 2023, 45(10): 3578-3586. doi: 10.11999/JEIT220991

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Research on Bidirectional Attenuation Loss Method for Rotating Object Detection in Remote Sensing Image

doi: 10.11999/JEIT220991

School of Information Science and Technology, North China University of Technology, Beijing 100144, China

Funds: National Key Research and Development Program (2020YFB1600704)

Received Date: 2022-07-26
Rev Recd Date: 2023-03-30

Available Online: 2023-04-04

Publish Date: 2023-10-31

Abstract

Abstract

Object detection in remote sensing image is one of the hot research topics in the field of remote sensing. In order to adapt to complex backgrounds and multi-directional objects in remote sensing images, the mainstream object detection model uses rotation detection method. However, most of positioning losses used for rotation detection generally has the problem that its trend is inconsistent with the trend of SkewIoU(Skew Intersection-over-Union). To solve this problem, a new bidirectional attenuation loss for rotating object detection is designed. Specifically, this method simulates SkewIoU by Gaussian product, and attenuates the product from two directions according to the deviation of the predicted position. The bidirectional attenuation loss has stronger trend-level alignment with SkewIoU and works better compared with other methods, thanks to its ability to reflect the SkewIoU change caused by position deviation. Experiments on DOTAv1.0 show the effectiveness of this method of various loss forms and different accuracy conditions.
- Remote sensing image,
- Object detection,
- Deep neural network,
- Loss function

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References(23)

References

[1]	李晓博, 孙文方, 李立. 静止轨道遥感卫星海面运动舰船快速检测方法[J]. 电子与信息学报, 2015, 37(8): 1862–1867. doi: 10.11999/JEIT141615 LI Xiaobo, SUN Wenfang, and LI Li. Ocean moving ship detection method for remote sensing satellite in geostationary orbit[J]. Journal of Electronics &Information Technology, 2015, 37(8): 1862–1867. doi: 10.11999/JEIT141615
[2]	陈琪, 陆军, 赵凌君, 等. 基于特征的SAR遥感图像港口检测方法[J]. 电子与信息学报, 2010, 32(12): 2873–2878. doi: 10.3724/SP.J.1146.2010.00079 CHEN Qi, LU Jun, ZHAO Lingjun, et al. Harbor detection method of SAR remote sensing images based on feature[J]. Journal of Electronics &Information Technology, 2010, 32(12): 2873–2878. doi: 10.3724/SP.J.1146.2010.00079
[3]	李轩, 刘云清. 基于似圆阴影的光学遥感图像油罐检测[J]. 电子与信息学报, 2016, 38(6): 1489–1495. doi: 10.11999/JEIT151334 LI Xuan and LIU Yunqing. Oil tank detection in optical remote sensing imagery based on quasi-circular shadow[J]. Journal of Electronics &Information Technology, 2016, 38(6): 1489–1495. doi: 10.11999/JEIT151334
[4]	ZHANG Zheng, MIAO Chunle, LIU Chang’an, et al. DCS-TransUperNet: Road segmentation network based on CSwin transformer with dual resolution[J]. Applied Sciences, 2022, 12(7): 3511. doi: 10.3390/app12073511
[5]	ZHANG Zheng, XU Zhiwei, LIU Chang’an, et al. Cloudformer: Supplementary aggregation feature and mask-classification network for cloud detection[J]. Applied Sciences, 2022, 12(7): 3221. doi: 10.3390/app12073221
[6]	GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014: 580–587.
[7]	GIRSHICK R. Fast R-CNN[C]. 2015 IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 1440–1448.
[8]	REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1149. doi: 10.1109/TPAMI.2016.2577031
[9]	REDMON J and FARHADI A. YOLOv3: An incremental improvement[J]. arXiv preprint arXiv: 1804.02767, 2018.
[10]	BOCHKOVSKIY A, WANG C Y, and LIAO H Y M. YOLOv4: Optimal speed and accuracy of object detection[J]. arXiv preprint arXiv: 2004.10934, 2020.
[11]	邵延华, 张铎, 楚红雨, 等. 基于深度学习的YOLO目标检测综述[J]. 电子与信息学报, 2022, 44(10): 3697–3708. doi: 10.11999/JEIT210790 SHAO Yanhua, ZHANG Duo, CHU Hongyu, et al. A review of YOLO object detection based on deep learning[J]. Journal of Electronics &Information Technology, 2022, 44(10): 3697–3708. doi: 10.11999/JEIT210790
[12]	LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: Single shot MultiBox detector[C]. 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 21–37.
[13]	LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(2): 318–327. doi: 10.1109/TPAMI.2018.2858826
[14]	DING Jian, XUE Nan, LONG Yang, et al. Learning RoI transformer for oriented object detection in aerial images[C]. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, 2019: 2844–2853.
[15]	YANG Xue, YAN Junchi, FENG Ziming, et al. R3Det: Refined single-stage detector with feature refinement for rotating object[C]. Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021: 3163–3171.
[16]	YANG Xue, HOU Liping, ZHOU Yue, et al. Dense label encoding for boundary discontinuity free rotation detection[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 15814–15824.
[17]	CHEN Zhiming, CHEN Ke’an, LIN Weiyao, et al. PIoU loss: Towards accurate oriented object detection in complex environments[C]. 16th European Conference on Computer Vision, Glasgow, UK, 2020: 195–211.
[18]	YANG Xue, YAN Junchi, MING Qi, et al. Rethinking rotated object detection with Gaussian Wasserstein distance loss[C]. Proceedings of the 38th International Conference on Machine Learning, 2021: 11830–11841.
[19]	YANG Xue, YANG Xiaojiang, YANG Jirui, et al. Learning high-precision bounding box for rotated object detection via kullback-leibler divergence appendix[C]. 35th Conference on Neural Information Processing Systems, 2021.
[20]	YANG Xue, ZHOU Yue, ZHANG Gefan, et al. The KFIoU loss for rotated object detection[J]. arXiv preprint arXiv: 2201.12558, 2022.
[21]	XIA Guisong, BAI Xiang, DING Jian, et al. DOTA: A large-scale dataset for object detection in aerial images[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 3974–3983.
[22]	EVERINGHAM M, VAN GOOL L, WILLIAMS C K I, et al. The PASCAL visual object classes (VOC) challenge[J]. International Journal of Computer Vision, 2010, 88(2): 303–338. doi: 10.1007/s11263-009-0275-4
[23]	LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: Common objects in context[C]. 13th European Conference on Computer Vision, Zurich, Switzerland, 2014: 740–755.