journal article·2024Open access

Target Detection on Water Surfaces Using Fusion of Camera and LiDAR Based Information

Yongguo Li, Yuanrong Wang, Jia Xie, Caiyin Xu, Kun Zhang

Computers, Materials & Continua· Volume 80· Issue 1

Abstract

To address the challenges of missed detections in water surface target detection using solely visual algorithms in unmanned surface vehicle (USV) perception, this paper proposes a method based on the fusion of visual and LiDA... | Find, read and cite all the research you need on Tech Science Press
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Research context

OpenAlex topics: Remote Sensing and LiDAR Applications

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References (34)

Show source-provided references
1. A lightweight multiscale-multiobject deep segmentation architecture for UAV-based consumer applications10.1109/TCE.2024.3367531 ↗
2. Intelligent rudder control of an unmanned surface vessel10.1016/j.eswa.2016.01.057 ↗
3. Water target recognition method and application for unmanned surface vessels10.1109/ACCESS.2021.3138983 ↗
4. An underwater target detection algorithm based on attention mechanism and improved YOLOv7
5. Gradient-based learning applied to document recognition
6. Ship detection and recognition based on improved YOLOv7
7. YOLOv7-CSAW for maritime target detection10.3389/fnbot.2023.1210470 ↗
8. The empirical application of automotive 3D radar sensor for target detection for an autonomous surface vehicle’s navigation10.3390/rs11101156 ↗
9. Obstacle recognition method for ship based on 3D lidar
10. Point cloud preprocessing on 3D LiDAR data for unmanned surface vehicle in marine environment
11. Ship target detection algorithm based on improved YOLOv510.3390/jmse9080908 ↗
12. Target fusion detection of LiDAR and camera based on the improved YOLO algorithm10.3390/math6100213 ↗
13. Research on unmanned surface vessel perception algorithm based on multi-sensor fusion
14. Water surface targets detection based on the fusion of vision and LiDAR10.3390/s23041768 ↗
15. C. Y. Wang, A. Bochkovskiy, and H. Y. M. Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” presented at the Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2023, pp. 7464–7475. doi: 10.1109/CVPR52729.2023.00721.10.1109/CVPR52729.2023.00721 ↗
16. ultralytics/yolov5: V5.0-YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube integrations
17. Z. Ge, S. Liu, F. Wang, Z. Li, and J. Sun, “YOLOX: Exceeding YOLO series in 2021,” Aug. 05, 2021. doi: 10.48550/arXiv.2107.08430.
18. C. Y. Wang, I. H. Yeh, and H. Y. M. Liao, “You only learn one representation: Unified network for multiple tasks,” May 10, 2021. doi: 10.48550/arXiv.2105.04206.
19. RepVGG: Making VGG-style ConvNets great again
20. Learning both weights and connections for efficient neural network
21. BinaryConnect: Training deep neural networks with binary weights during propagations
22. G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” Mar. 09, 2015. doi: 10.48550/arXiv.1503.02531.
23. A. G. Howard et al., “MobileNets: Efficient convolutional neural networks for mobile vision applications,” Apr. 16, 2017. doi: 10.48550/arXiv.1704.04861.
24. X. Zhang, X. Zhou, M. Lin, and J. Sun, “ShuffleNet: An extremely efficient convolutional neural network for mobile devices,” Dec. 07, 2017. doi: 10.48550/arXiv.1707.01083.10.1109/CVPR.2018.00716 ↗
25. GhostNet: More features from cheap operations
26. Q. Han et al., “On the connection between local attention and dynamic depth-wise convolution,” Aug. 04, 2022. doi: 10.48550/arXiv.2106.04263.
27. A sensor fusion system with thermal infrared camera and LiDAR for autonomous vehicles and deep learning based object detection10.1016/j.icte.2021.12.016 ↗
28. Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles10.1007/s11554-024-01436-6 ↗
29. UnitBox: An advanced object detection network
30. Enhancing geometric factors in model learning and inference for object detection and instance segmentation10.1109/TCYB.2021.3095305 ↗
31. M. Siliang and X. Yong, “MPDIoU: A loss for efficient and accurate bounding box regression,” 2023. doi: 10.48550/arXiv.2307.07662.
32. Z. Yu, H. Huang, W. Chen, Y. Su, Y. Liu and X. Wang, “YOLO-FaceV2: A scale and occlusion aware face detector,” 2022. doi: 10.48550/arXiv.2208.02019.
33. A flexible new technique for camera calibration10.1109/34.888718 ↗
34. Automatic targetless monocular camera and LiDAR external parameter calibration method for mobile robots10.3390/rs15235560 ↗