Ship Detection and Recognition Based on Improved YOLOv7
Source abstract
Abstract
In this paper, an advanced YOLOv7 model is proposed to tackle the challenges associated with ship detection and recognition tasks, such as the irregular shapes and varying sizes of ships. The improved model replaces the fixed... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Infrared Target Detection Methodologies · Advanced Image and Video Retrieval Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (35)
Show source-provided references
1. Ship recognition based on Hu invariant moments and convolutional neural network for video surveillance10.1007/s11042-020-09574-2 ↗
2. A high resolution optical satellite image dataset for ship recognition and some new baselines
3. Comparison of two deep learning methods for ship target recognition with optical remotely sensed data10.1007/s00521-020-05307-6 ↗
4. Molecular mechanisms of brain water transport
5. Efficient water transport and solar steam generation via radially, hierarchically structured aerogels
6. Past, present, and future of the satellite-based automatic identification system: Areas of applications (2004–2016)10.1007/s13437-018-0151-6 ↗
7. How big data enriches maritime research–a critical review of automatic identification system (AIS) data applications10.1080/01441647.2019.1649315 ↗
8. Estimation of berthing state of maritime autonomous surface ships based on 3D LiDAR10.1016/j.oceaneng.2022.111131 ↗
9. A robust autonomous navigation and mapping system based on GPS and LiDAR data for unconstraint environment10.1007/s12145-022-00791-x ↗
10. Automatic generation of water route based on AIS big data and ECDIS10.1007/s12517-021-06930-w ↗
11. Route planning and tracking for ships based on the ECDIS platform10.1109/ACCESS.2021.3078899 ↗
12. Ship classification using deep learning techniques for maritime target tracking
13. Deep learning for autonomous ship-oriented small ship detection10.1016/j.ssci.2020.104812 ↗
14. A deep learning model to extract ship size from sentinel-1 SAR images
15. HOG-ShipCLSNet: A novel deep learning network with hog feature fusion for SAR ship classification
16. Rich feature hierarchies for accurate object detection and semantic segmentation
17. OverFeat: Integrated recognition, localization and detection using convolutional networks
18. Selective search for object recognition10.1007/s11263-013-0620-5 ↗
19. Spatial pyramid pooling in deep convolutional networks for visual recognition
20. Fast r-cnn
21. Faster r-cnn: Towards real-time object detection with region proposal networks
22. Few-shot object detection with attention-RPN and multi-relation detector
23. Mask r-cnn
24. You only look once: Unified, real-time object detection
25. YOLO9000: Better, faster, stronger
26. J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” arXiv preprint arXiv:1804.02767, 2018.
27. A. Bochkovskiy, C. Wang and H. Y. Mark Liao, “Yolov4: Optimal speed and accuracy of object detection,” arXiv preprint arXiv:2004.10934, 2020.
28. C. Li, L. Li, H. Jiang, K. Weng, Y. Geng et al., “YOLOv6: A single-stage object detection framework for industrial applications,” arXiv preprint arXiv:2209.02976, 2022.
29. C. Wang, A. Bochkovskiy and H. Y. Mark Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” arXiv preprint arXiv:2207.02696, 2022.10.1109/CVPR52729.2023.00721 ↗
30. SSD: Single shot multibox detector
31. Scale-transferrable object detection
32. Blitznet: A real-time deep network for scene understanding
33. M2det: A single-shot object detector based on multi-level feature pyramid network
34. Deep feature pyramid reconfiguration for object detection
35. Seaships: A large-scale precisely annotated dataset for ship detection10.1109/TMM.2018.2865686 ↗