A Dense Feature Pyramid Network for Remote Sensing Object Detection
Source abstract
Abstract
In recent years, object detection in remote sensing images has become a popular topic in computer vision research. However, there are various problems in remote sensing object detection, such as complex scenes, small objects in large fields of view, and multi-scale object in different categories. To address these issues, we propose DFPN-YOLO, a dense feature pyramid network for remote sensing object detection. To address difficulties in detecting small objects in large scenes, we add a larger detection layer on top of the three detection layers of YOLOv3, and we propose Dense-FPN, a dense feature pyramid network structure that enables all four detection layers to combine semantic information before sampling and after sampling to improve the performance of object detection at different scales. In addition, we add an attention module in the residual blocks of the backbone to allow the network to quickly extract key feature information in complex scenes. The results show that the mean average precision (mAP) of our method on the RSOD datasets reached 92%, which is 8% higher than the mAP of YOLOv3, and the mAP increased from 62.41% on YOLOv3 to 69.33% with our method on the DIOR datasets, outperforming even YOLOv4.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Remote-Sensing Image Classification · Advanced Image and Video Retrieval Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Electronics and Communications Engineering, North China University of Technology, Beijing 100144, China
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Qian Xuesen Laboratory of Space Technology, China Academy of Space Technology, Beijing 100094, China
Funding
National Natural Science Foundation of China
References (42)
Show source-provided references
1. Chen, W., Zhou, S., Pan, Z., Zheng, H., and Liu, Y. (2019). Mapless Collaborative Navigation for a Multi-Robot System Based on the Deep Reinforcement Learning. Appl. Sci., 9.10.3390/app9204198 ↗
2. Tang, S., and Chen, Z. (2021). Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning. Appl. Sci., 11.10.3390/app11093952 ↗
3. Zhao, Y., Deng, X., and Lai, H. (2020). A Deep Learning-Based Method to Detect Components from Scanned Structural Drawings for Reconstructing 3D Models. Appl. Sci., 10.10.3390/app10062066 ↗
4. Simonyan, K., Vedaldi, A., and Zisserman, A. (2013). Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps. arXiv.
5. A Review on Image Segmentation Techniques for Future Research Study10.14445/22315381/IJETT-V35P301 ↗
6. A new three-step search algorithm for block motion estimation
7. Benfold, B., and Reid, I. (2011, January 20–25). Stable multi-target tracking in real-time surveillance video. Proceedings of the Computer Vision & Pattern Recognition (CVPR 2011), Colorado Springs, CO, USA.10.1109/CVPR.2011.5995667 ↗
8. A Survey on Object Detection in Optical Remote Sensing Images10.1016/j.isprsjprs.2016.03.014 ↗
9. Lin, T.Y., Goyal, P., Girshick, R., He, K., and Dollár, P. (2017, January 22–29). Focal Loss for Dense Object Detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.10.1109/ICCV.2017.324 ↗
10. Object Detection with Discriminatively Trained Part-Based Models10.1109/TPAMI.2009.167 ↗
11. Divvala, S.K., Efros, A.A., and Hebert, M. (2012, January 7–13). How important are Deformable Parts in the Deformable Parts Model?. Proceedings of the European Conference on Computer Vision, Florence, Italy.10.1007/978-3-642-33885-4_4 ↗
12. Dalal, N., and Triggs, B. (2005, January 20–26). Histograms of oriented gradients for human detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA.
13. Support vector machines for classification and regression
14. Regulated nucleo/cytoplasmic exchange of HOG1 MAPK requires the importin β homologs NMD5 and XPO110.1093/emboj/17.19.5606 ↗
15. The CNN universal machine: An analogic array computer10.1109/82.222815 ↗
16. Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv.
17. Dense Attention Pyramid Networks for Multi-Scale Ship Detection in SAR Images10.1109/TGRS.2019.2923988 ↗
18. Huang, W., Li, G., Chen, Q., Ju, M., and Qu, J. (2021). CF2PN: A Cross-Scale Feature Fusion Pyramid Network Based Remote Sensing Target Detection. Remote Sens., 13.10.3390/rs13050847 ↗
19. Xu, D., and Wu, Y. (2021). FE-YOLO: A Feature Enhancement Network for Remote Sensing Target Detection. Remote Sens., 13.10.3390/rs13071311 ↗
20. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23–28). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.10.1109/CVPR.2014.81 ↗
21. Girshick, R. (2015, January 7–13). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.10.1109/ICCV.2015.169 ↗
22. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks10.1109/TPAMI.2016.2577031 ↗
23. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Fu, C., and Berg, A.C. (2016, January 8–16). SSD: Single Shot Multibox Detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.10.1007/978-3-319-46448-0_2 ↗
24. RScott (2016). FCLIP demos improved SSDS detect-to-engage co-ordination. Jane’s Int. Def. Rev., 49, 17.
25. An intelligent water level monitoring method based on SSD algorithm10.1016/j.measurement.2021.110047 ↗
26. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
27. Shaifee, M.J., Chywl, B., Li, F., and Wong, A. (2017). Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video. arXiv.10.15353/vsnl.v3i1.171 ↗
28. Chen, Q., Wang, Y., Yang, T., Zhang, X., Cheng, J., and Sun, J. (2021, January 20–25). You only look one-level feature. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.10.1109/CVPR46437.2021.01284 ↗
29. Tan, M., and Le, Q. (2019, January 9–15). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, PMLR 2019, Long Beach, CA, USA.
30. Tan, M., Pang, R., and Le, Q.V. (2020, January 14–19). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.10.1109/CVPR42600.2020.01079 ↗
31. Redmon, J., and Farhadi, A. (2017, January 21–26). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.690 ↗
32. Bochkovskiy, A., Wang, C.Y., and Liao, H. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv.
33. Zhu, X., Lyu, S., Wang, X., and Zhao, Q. (2021, January 11–17). TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, Montreal, BC, Canada.10.1109/ICCVW54120.2021.00312 ↗
34. Li, X., Hu, X., and Yang, J. (2019). Spatial group-wise enhance: Improving semantic feature learning in convolutional networks. arXiv.
35. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
36. Wang, H., Zhang, F., and Wang, L. (2020, January 11–12). Fruit classification model based on improved Darknet53 convolutional neural network. Proceedings of the 2020 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), Vientiane, Laos.10.1109/ICITBS49701.2020.00194 ↗
37. Lin, T.Y., Goyal, P., Girshick, R., He, K., and Dollár, P. (2017, January 22–29). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Venice, Italy.10.1109/CVPR.2017.106 ↗
38. Elliptic Fourier transformation-based histograms of oriented gradients for rotationally invariant object detection in remote-sensing images10.1080/01431161.2014.999881 ↗
39. Object detection in optical remote sensing images: A survey and a new benchmark10.1016/j.isprsjprs.2019.11.023 ↗
40. The pascal visual object classes (voc) challenge10.1007/s11263-009-0275-4 ↗
41. Learning Rotation-Invariant Convolutional Neural Networks for Target detection in VHR Optical Remote Sensing Images10.1109/TGRS.2016.2601622 ↗
42. Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv.