An Improved Method for Ship Target Detection Based on YOLOv4
Source abstract
Abstract
The resolution of remote sensing images has increased with the maturation of satellite technology. Ship detection technology based on remote sensing images makes it possible to monitor a large range and far sea area, which can greatly enrich the monitoring means of maritime departments. In this paper, we conducted research on small target detection and resistance to complex background interference. First, a ship dataset with four types of targets (aircraft carriers, warships, merchant ships and submarines) is constructed, and experiments are conducted on the dataset using the object detection algorithm YOLOv4. The Kmeans++ clustering algorithm is used for a priori frame selection, and the migration learning method is used to enhance the detection effect of the YOLOv4. Second, the model is improved to address the problems of missed detection of small ships and difficulty in resisting background interference: the RFB_s (Receptive Field Block) with dilated convolution is introduced instead of the SPP (Spatial Pyramid Pooling) to enlarge the receptive field and improve the detection of small targets; the attention mechanism CBAM (Convolutional Block Attention Module) is added to adjust the weights of different features to highlight salient features useful for ship detection task, which improve the detection performance of small ships and improve the model’s ability to resist complex background. Compared to YOLOv4, our proposed model achieved a large improvement in mAP (mean Average Precision) from 77.66% to 91.40%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Infrared Target Detection Methodologies · Remote-Sensing Image Classification
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100049, China
Funding
National Key R&D Program of China · National Natural Science Foundation of China
References (28)
Show source-provided references
1. Ship detection and classification from optical remote sensing images: A survey10.1016/j.cja.2020.12.013 ↗
2. Vessel detection and classification from spaceborne optical images: A literature survey10.1016/j.rse.2017.12.033 ↗
3. 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 ↗
4. Girshick, R. (2015, January 7–12). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Boston, MA, USA.10.1109/ICCV.2015.169 ↗
5. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks10.1109/TPAMI.2016.2577031 ↗
6. Liu, W., Anguelov, D., and Erhan, D. (2016, January 11–14). SSD: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.10.1007/978-3-319-46448-0_2 ↗
7. 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, Las Vegas, NV, USA.10.1109/CVPR.2016.91 ↗
8. 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 ↗
9. Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv.
10. Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv.
11. Yao, S., and Chen, Y. (2020). An improved algorithm for detecting pneumonia based on YOLOv3. Appl. Sci., 10.10.3390/app10051818 ↗
12. Rodríguez-Rangel, H., and Morales-Rosales, L.A. (2022). Analysis of Statistical and Artificial Intelligence Algorithms for Real-Time Speed Estimation Based on Vehicle Detection with YOLO. Appl. Sci., 12.10.3390/app12062907 ↗
13. Liu, S., and Huang, D. (2018, January 8–14). Receptive field block net for accurate and fast object detection. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01252-6_24 ↗
14. Woo, S., Park, J., and Lee, J.Y. (2018, January 8–14). CBAM: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.10.1007/978-3-030-01234-2_1 ↗
15. Spatial pyramid pooling in deep convolutional networks for visual recognition10.1109/TPAMI.2015.2389824 ↗
16. Zhang, T., Zhang, X., and Ke, X. (2021). Quad-FPN: A novel quad feature pyramid network for SAR ship detection. Remote Sens., 13.10.3390/rs13142771 ↗
17. Qing, Y., Liu, W., Feng, L., and Gao, W. (2021). Improved Yolo network for free-angle remote sensing target detection. Remote Sens., 13.10.3390/rs13112171 ↗
18. Deep learning for autonomous ship-oriented small ship detection10.1016/j.ssci.2020.104812 ↗
19. Arbitrary-oriented ship detection framework in optical remote-sensing images10.1109/LGRS.2018.2813094 ↗
20. 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 ↗
21. Guo, J., and Wang, S. (2022). Saliency Guided DNL-Yolo for Optical Remote Sensing Images for Off-Shore Ship Detection. Appl. Sci., 12.10.3390/app12052629 ↗
22. Liu, S., Qi, L., and Qin, H. (2018, January 18–23). Path aggregation network for instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2018.00913 ↗
23. Zheng, Z., Wang, P., and Liu, W. (2020, January 7–12). Distance-IOU loss: Faster and better learning for bounding box regression. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.10.1609/aaai.v34i07.6999 ↗
24. Szegedy, C., Liu, W., and Jia, Y. (2015, January 7–12). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.10.1109/CVPR.2015.7298594 ↗
25. Adaptive feature fusion with attention mechanism for multi-scale target detection10.1007/s00521-020-05150-9 ↗
26. Liu, Z., Yuan, L., Weng, L., and Yang, Y. (2017, January 24–26). A high resolution optical satellite image dataset for ship recognition and some new baselines. Proceedings of the International Conference on Pattern Recognition Applications and Methods, Porto, Portugal.10.5220/0006120603240331 ↗
27. Deng, J., Dong, W., and Socher, R. (2009, January 20–25). Imagenet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.10.1109/CVPR.2009.5206848 ↗
28. Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv.10.1109/CVPR52729.2023.00721 ↗