Improved Detector Based on Yolov5 for Typical Targets on the Sea Surfaces
Source abstract
Abstract
Detection of targets on sea surfaces is an important area of application that can bring great benefits to the management and control systems in marine environments. However, there are few open-source datasets accessible for the purpose of object detection on seas and rivers. In this paper, a study is conducted on the improved detection algorithms based on the YOLOv5 model. The dataset for the tests contains ten categories of typical objects that are commonly seen in the contexts of seas, including ships, devices, and structures. Multiple augmentation methods are employed in the pre-processing of the input data, which are verified to be effective in enhancing the generalization ability of the algorithm. Moreover, a new form of the loss function is proposed that highlights the effects of the high-quality boxes during training. The results demonstrate that the adapted loss function contributes to a boost in the model performance. According to the ablation studies, the synthesized methods raise the inference accuracy by making up for several shortcomings of the baseline model for the detection tasks of single or multiple targets from varying backgrounds.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Maritime Navigation and Safety · Infrared Target Detection Methodologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
China Ship Scientific Research Center, No. 222 East Shanshui Road, Binhu District, Wuxi 214082, China
Funding
Ministry of Science and Technology · National Key Research and Development Program of China · Ministry of Industry and Information Technology
References (28)
Show source-provided references
1. Ship detection with deep learning: A survey
2. He, Y., Zhu, C., Wang, J., Savvides, M., and Zhang, X. (2018, January 18–23). Bounding Box Regression With Uncertainty for Accurate Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.10.1109/CVPR.2019.00300 ↗
3. Benjumea, A., Teeti, I., Cuzzolin, F., and Bradley, A. (2021). YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles. arXiv.
4. Zhang, H., Chang, H., Ma, B., Wang, N., and Chen, X. (2020). Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training. arXiv.10.1007/978-3-030-58555-6_16 ↗
5. Girshick, R.B., Donahue, J., Darrell, T., and Malik, J. (2013, January 23–28). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.10.1109/CVPR.2014.81 ↗
6. Girshick, R.B. (2015, January 7–13). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.10.1109/ICCV.2015.169 ↗
7. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks10.1109/TPAMI.2016.2577031 ↗
8. Dai, J., Li, Y., He, K., and Sun, J. (2016). R-FCN: Object Detection via Region-based Fully Convolutional Networks. arXiv.
9. Cai, Z., and Vasconcelos, N. (2017, January 21–26). Cascade R-CNN: Delving Into High Quality Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2018.00644 ↗
10. Mask R-CNN10.1109/TPAMI.2018.2844175 ↗
11. Redmon, J., Divvala, S.K., Girshick, R.B., and Farhadi, A. (2015, January 7–12). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.10.1109/CVPR.2016.91 ↗
12. Jocher, G. (2022, September 01). Yolov5. Available online: https://github.com/ultralytics/yolov5.
13. 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 European Conference on Computer Vision, Amsterdam, The Netherlands.10.1007/978-3-319-46448-0_2 ↗
14. Focal Loss for Dense Object Detection10.1109/TPAMI.2018.2858826 ↗
15. The Pascal Visual Object Classes (VOC) Challenge10.1007/s11263-009-0275-4 ↗
16. Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art10.1016/j.eswa.2022.116793 ↗
17. Lin, T.-Y., Dollár, P., Girshick, R.B., He, K., Hariharan, B., and Belongie, S.J. (2016, January 27–30). Feature Pyramid Networks for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2017.106 ↗
18. Research on ship detection of optical remote sensing image based on Yolo V510.1088/1742-6596/2215/1/012027 ↗
19. Marine ship detection and classification based on YOLOv5 model10.1088/1742-6596/2181/1/012025 ↗
20. Pang, L., Li, B., Zhang, F., Meng, X., and Zhang, L. (2022). A Lightweight YOLOv5-MNE Algorithm for SAR Ship Detection. Sensors, 22.10.3390/s22187088 ↗
21. Lei, F., Tang, F., and Li, S. (2022). Underwater Target Detection Algorithm Based on Improved YOLOv5. J. Mar. Sci. Eng., 10.10.3390/jmse10030310 ↗
22. Liu, Z., Zhuang, Y., Jia, P., Wu, C., Xu, H., and Liu, Z. (2022). A Novel Underwater Image Enhancement Algorithm and an Improved Underwater Biological Detection Pipeline. J. Mar. Sci. Eng., 10.10.3390/jmse10091204 ↗
23. 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 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.10.1109/CVPR.2019.00075 ↗
24. 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 Conference on Artificial Intelligence, New York, NY, USA.10.1609/aaai.v34i07.6999 ↗
25. Focal and Efficient IOU Loss for Accurate Bounding Box Regression10.1016/j.neucom.2022.07.042 ↗
26. Peng, C., Xiao, T., Li, Z., Jiang, Y., Zhang, X., Jia, K., Yu, G., and Sun, J. (2017, January 21–26). MegDet: A Large Mini-Batch Object Detector. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2018.00647 ↗
27. Zhang, Z., He, T., Zhang, H., Zhang, Z., Xie, J., and Li, M. (2019). Bag of Freebies for Training Object Detection Neural Networks. arXiv.
28. Buslaev, A.V., Parinov, A., Khvedchenya, E., Iglovikov, V.I., and Kalinin, A.A. (2018). Albumentations: Fast and flexible image augmentations. arXiv.