YOLO-Based Fish Detection in Underwater Environments
Author:
Affiliation:
1. Perception, Robotics and Intelligent Machines Research Lab (PRIME), Department of Computer Science, Universite de Moncton, 18 Antonine-Maillet Ave, Moncton, NB E1A 3E9, Canada
Publisher
MDPI
Link
https://www.mdpi.com/2673-4931/29/1/44/pdf
Reference7 articles.
1. Kay, J., Kulits, P., Stathatos, S., Deng, S., Young, E., Beery, S., Van Horn, G., and Perona, P. (2022, January 23–27). The Caltech Fish Counting Dataset: A Benchmark for Multiple-Object Tracking and Counting. Proceedings of the 17th European Conference, Tel Aviv, Israel.
2. Temperate fish detection and classification: A deep learning based approach;Wiklund;Appl. Intell.,2022
3. Underwater fish detection using deep learning for water power applications;Xu;CSCI,2018
4. Automated fish detection and classification on sonar images using detection transformer and YOLOv7;Mahoro;SPIE,2023
5. Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 17–24). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, Canada.
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