KCFS-YOLOv5: A High-Precision Detection Method for Object Detection in Aerial Remote Sensing Images
Source abstract
Abstract
Aerial remote sensing image object detection, based on deep learning, is of great significance in geological resource exploration, urban traffic management, and military strategic information. To improve intractable problems in aerial remote sensing image, we propose a high-precision object detection method based on YOLOv5 for aerial remote sensing image. The object detection method is called KCFS-YOLOv5. To obtain the appropriate anchor box, we used the K-means++ algorithm to optimize the initial clustering points. To further enhance the feature extraction and fusion ability of the backbone network, we embedded the Coordinate Attention (CA) in the backbone network of YOLOv5 and introduced the Bidirectional Feature Pyramid Network (BiFPN) in the neck network of conventional YOLOv5. To improve the detection precision of tiny objects, we added a new tiny object detection head based on the conventional YOLOv5. To reduce the deviation between the predicted box and the ground truth box, we used the SIoU Loss function. Finally, we fused and adjusted the above improvement points and obtained high-precision detection method: KCFS-YOLOv5. This detection method was evaluated on three datasets (NWPU VHR-10, RSOD, and UCAS-AOD-CAR). The comparative experiment results demonstrate that our KCFS-YOLOv5 has the highest accuracy for the object detection in aerial remote sensing image.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Advanced Image and Video Retrieval Techniques · Robotics and Sensor-Based Localization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450002, China
College of Data Target Engineering, PLA Information Engineering University, Zhengzhou 450001, China
Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan 430079, China
Funding
Jie Huang
References (54)
Show source-provided references
1. Zhao, D., Xie, D., Yin, F., Liu, L., Feng, J., and Ashraf, T.T.M. (2022). Estimation of Pb Content Using Reflectance Spectroscopy in Farmland Soil near Metal Mines, Central China. Remote Sens., 14.10.3390/rs14102420 ↗
2. Chen, Z., Su, R., Wang, Y., Chen, G., Wang, Z., Yin, P., and Wang, J. (2022). Automatic Estimation of Apple Orchard Blooming Levels Using the Improved YOLOv5. Agronomy, 12.10.3390/agronomy12102483 ↗
3. Wahyudi Sumari, A.D., Pranata, A.S., Mashudi, I.A., Syamsiana, I.N., and Sereati, C.O. (2022, January 10–11). Automatic Target Recognition and Identification for Military Ground-to-Air Observation Tasks using Support Vector Machine and Information Fusion. Proceedings of the 2022 International Conference on ICT for Smart Society (ICISS), Virtual.10.1109/ICISS55894.2022.9915256 ↗
4. Dynamic simulation of land use change and assessment of carbon storage based on climate change scenarios at the city level: A case study of Bortala, China10.1016/j.ecolind.2021.108499 ↗
5. Liu, Y., Yao, X., Gu, Z., Zhou, Z., Liu, X.S., Chen, X., and Wei, S. (2022). Study of the Automatic Recognition of Landslides by Using InSAR Images and the Improved Mask R-CNN Model in the Eastern Tibet Plateau. Remote Sens., 14.10.3390/rs14143362 ↗
6. Meng, J., Yan, J., and Zhao, J. (2022). Bubble Plume Target Detection Method of Multibeam Water Column Images Based on Bags of Visual Word Features. Remote Sens., 14.10.3390/rs14143296 ↗
7. Identification of Tropical Cyclone Centers in SAR Imagery Based on Template Matching and Particle Swarm Optimization Algorithms10.1109/TGRS.2018.2863259 ↗
8. Jian, S., Jiang, J., Lu, K., and Zhang, Y. (2014, January 19–23). SEU-tolerant Restricted Boltzmann Machine learning on DSP-based fault detection. Proceedings of the 2014 12th International Conference on Signal Processing (ICSP), Hangzhou, China.10.1109/ICOSP.2014.7015250 ↗
9. 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 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.10.1109/CVPR.2014.81 ↗
10. Girshick, R. (2015, January 7–13). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.10.1109/ICCV.2015.169 ↗
11. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks10.1109/TPAMI.2016.2577031 ↗
12. 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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
13. Redmon, J., and Farhadi, A. (2017, January 21–26). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.690 ↗
14. Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv.
15. Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv.
16. Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., Myrland, Ø, and Kummervold, P.E. (2021). Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5. Algorithms, 14.10.3390/a14040114 ↗
17. Ge, Z., Liu, S., Wang, F., Li, Z., and Sun, J. (2021). YOLOX: Exceeding YOLO Series in 2021. arXiv.
18. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S.E., Fu, C.Y., and Berg, A.C. (2016, January 11–14). SSD: Single Shot MultiBox Detector. Proceedings of the ECCV, Amsterdam, The Netherlands.10.1007/978-3-319-46448-0_2 ↗
19. Fu, C.Y., Liu, W., Ranga, A., Tyagi, A., and Berg, A.C. (2017). DSSD: Deconvolutional Single Shot Detector. arXiv.
20. Yan, D., Li, G., Li, X., Zhang, H., Lei, H., Lu, K., Cheng, M., and Zhu, F. (2021). An Improved Faster R-CNN Method to Detect Tailings Ponds from High-Resolution Remote Sensing Images. Remote Sens., 13.10.3390/rs13112052 ↗
21. Aircraft Target Detection in Remote Sensing Images Based on Improved YOLOv510.1109/ACCESS.2022.3140876 ↗
22. Long, Y.G. (2022, November 01). RSOD Dataset. [EB/OL]. Available online: https://github.com/RSIA-LIESMARS-WHU/RSOD-Dataset-.
23. Cheng, G., and Han, J. (2022, November 01). NWPU VHR-10 Dataset. [EB/OL]. Available online: https://github.com/chaozhong2010/VHR-10_dataset_coco.
24. (2022, November 01). UCAS-AOD Dataset. [EB/OL]. Available online: https://hyper.ai/datasets/5419.
25. ImageNet Classification with Deep Convolutional Neural Networks10.1145/3065386 ↗
26. Da, Y., Gao, X., and Li, M. (2022, January 15–17). Remote Sensing Image Ship Detection Based on Improved YOLOv3. Proceedings of the 2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP), Xi’an, China.10.1109/ICSP54964.2022.9778531 ↗
27. Cao, C., Wu, J., Zeng, X., Feng, Z., Wang, T., Yan, X., Wu, Z., Wu, Q., and Huang, Z. (2020). Research on Airplane and Ship Detection of Aerial Remote Sensing Images Based on Convolutional Neural Network. Sensors, 20.10.3390/s20174696 ↗
28. Li, Z., Namiki, A., Suzuki, S., Wang, Q., Zhang, T., and Wang, W. (2022). Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5. Appl. Sci., 12.10.3390/app12168314 ↗
29. Wang, Z., Lu, H., Jin, J., and Hu, K. (2022). Human Action Recognition Based on Improved Two-Stream Convolution Network. Appl. Sci., 37.10.3390/app12125784 ↗
30. Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8–14). CBAM: Convolutional Block Attention Module. Proceedings of the ECCV, Munich, Germany.10.1007/978-3-030-01234-2_1 ↗
31. Yang, L., Yuan, G., Zhou, H., Liu, H., Chen, J., and Wu, H. (2022). RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images. Appl. Sci., 12.10.3390/app12178707 ↗
32. Wang, Q., Wu, B., Zhu, P.F., Li, P., Zuo, W., and Hu, Q. (2020, January 13–19). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.10.1109/CVPR42600.2020.01155 ↗
33. Akyon, F.C., Altinuc, S.O., and Temizel, A. (2022). Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection. arXiv.10.1109/ICIP46576.2022.9897990 ↗
34. Jaderberg, M., Simonyan, K., Zisserman, A., and Kavukcuoglu, K. (2015, January 12). Spatial Transformer Networks. Proceedings of the NIPS, Montreal, QC, Canada.
35. Squeeze-and-Excitation Networks10.1109/TPAMI.2019.2913372 ↗
36. Li, Y., Sun, S., Zhang, C., Yang, G., and Ye, Q. (2022). One-Stage Disease Detection Method for Maize Leaf Based on Multi-Scale Feature Fusion. Appl. Sci., 12.10.3390/app12167960 ↗
37. Lin, T.Y., Dollár, P., Girshick, R.B., He, K., Hariharan, B., and Belongie, S.J. (2017, January 21–26). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.10.1109/CVPR.2017.106 ↗
38. Yang, J., Fu, X., Hu, Y., Huang, Y., Ding, X., and Paisley, J.W. (2017, January 22–29). PanNet: A Deep Network Architecture for Pan-Sharpening. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.10.1109/ICCV.2017.193 ↗
39. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition10.1109/TPAMI.2015.2389824 ↗
40. Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18–22). Path Aggregation Network for Instance Segmentation. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00913 ↗
41. Arthur, D., and Vassilvitskii, S. (2007, January 7–9). k-means++: The advantages of careful seeding. Proceedings of the SODA ’07, New Orleans, LA, USA.
42. Hou, Q., Zhou, D., and Feng, J. (2021, January 20–25). Coordinate Attention for Efficient Mobile Network Design. Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.10.1109/CVPR46437.2021.01350 ↗
43. Tan, M., Pang, R., and Le, Q.V. (2020, January 13–19). 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 ↗
44. Gevorgyan, Z. (2022). SIoU Loss: More Powerful Learning for Bounding Box Regression. arXiv.
45. Rezatofighi, S.H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I.D., and Savarese, S. (2019, January 15–20). Generalized Intersection Over Union: A Metric and a Loss for Bounding Box Regression. Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.10.1109/CVPR.2019.00075 ↗
46. Chen, D., and Miao, D. (2021). Control Distance IoU and Control Distance IoU Loss Function for Better Bounding Box Regression. arXiv.
47. Liu, Y., Shao, Z., Teng, Y., and Hoffmann, N. (2021). NAM: Normalization-based Attention Module. arXiv.
48. Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020, January 7–12). Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression. Proceedings of the AAAI, New York, NY, USA.10.1609/aaai.v34i07.6999 ↗
49. CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery10.1109/TGRS.2019.2930982 ↗
50. An Anchor-Free Network With Density Map and Attention Mechanism for Multiscale Object Detection in Aerial Images
51. CANet: Centerness-Aware Network for Object Detection in Remote Sensing Images
52. Segmenting Objects From Relational Visual Data10.1109/TPAMI.2021.3115815 ↗
53. Li, X., Qin, Y., Wang, F., Guo, F., and Yeow, J.T.W. (2020, January 27–29). Pitaya detection in orchards using the MobileNet-YOLO model. Proceedings of the 2020 39th Chinese Control Conference (CCC), Shenyang, China.10.23919/CCC50068.2020.9189186 ↗
54. AB-DLM: An Improved Deep Learning Model Based on Attention Mechanism and BiFPN for Driver Distraction Behavior Detection10.1109/ACCESS.2022.3197146 ↗