Enhanced Tuna Detection and Automated Counting Method Utilizing Improved YOLOv7 and ByteTrack
Source abstract
Abstract
At present, catch statistics in the pelagic fishery industry rely mainly on manual counting methods. However, this method suffers from low statistical accuracy and insufficient timeliness. An automatic tuna counting approach based on ByteTrack and YOLOv7-Tuna is presented in this research. The method selects YOLOv7 as the base model, adopts DySnakeConv to obtain more temporal features, combines it with CoordConv to enhance the location-awareness ability of the model, and introduces DyHead to suppress the interference of complex backgrounds. The experimental results show that YOLOv7-Tuna outperforms YOLOv7 in terms of precision by 5.2%, recall by 3.1%, mAP@0.5 by 0.5%, and mAP@0.5:0.95 by 10%. Furthermore, the ByteTrack algorithm was employed to achieve real-time tracking of targets, with specific counting areas added. The results indicate that the counting error of this method decreased to 3.1%. It can effectively accomplish automatic counting tasks for tuna, providing a new solution for the automatic quantification of catch in the offshore fishing industry.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Industrial Vision Systems and Defect Detection · Vehicle License Plate Recognition
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China
Funding
National Key Research and Development Program for the 14th Five-Year Plan
References (26)
Show source-provided references
1. Strengthening monitoring, control and surveillance of human activities in marine areas beyond national jurisdiction: Challenges and opportunities for an international legally binding instrument10.1016/j.marpol.2020.103976 ↗
2. Assessing progress in data reporting by tuna Regional Fisheries Management Organizations10.1111/faf.12687 ↗
3. Automatic fish species classification using deep convolutional neural networks10.1007/s11277-019-06634-1 ↗
4. A modified YOLOv3 model for fish detection based on MobileNetv1 as backbone10.1016/j.aquaeng.2020.102117 ↗
5. Fish identification method based on FTVGG16 convolutional neural network
6. ImageNet classification with deep convolutional neural networks10.1145/3065386 ↗
7. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 8–10). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.10.1109/CVPR.2015.7298594 ↗
8. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
9. Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21–26). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.10.1109/CVPR.2017.243 ↗
10. Underwater Fish Species Identification Model and Real-Time Identification System
11. YOLO-Fish: A robust fish detection model to detect fish in realistic underwater environment10.1016/j.ecoinf.2022.101847 ↗
12. Ayuningtias, I., Jaya, I., and Iqbal, M. (2021, January 24–25). Identification of yellowfin tuna (Thunnus albacares), mackerel tuna (Euthynnus affinis), and skipjack tuna (Katsuwonus pelamis) using deep learning. Proceedings of the Earth and Environmental Science, Bogor, Indonesia.10.1088/1755-1315/944/1/012009 ↗
13. Automatic live fingerlings counting using computer vision10.1016/j.compag.2019.105015 ↗
14. Detecting and counting harvested fish and identifying fish types in electronic monitoring system videos using deep convolutional neural networks10.1093/icesjms/fsaa076 ↗
15. Counting method for cultured fishes based on multi-modules and attention mechanism10.1016/j.aquaeng.2021.102215 ↗
16. Kibet, D., and Shin, J.-H. (2023). Counting Abalone with High Precision Using YOLOv3 and DeepSORT. Processes, 11.10.3390/pr11082351 ↗
17. Zhang, S., Yang, X., Wang, Y., Zhao, Z., Liu, J., Liu, Y., Sun, C., and Zhou, C. (2020). Automatic fish population counting by machine vision and a hybrid deep neural network model. Animals, 10.10.3390/ani10020364 ↗
18. Qian, Z.-M., Chen, X., and Jiang, H. (2023, January 28–30). Fish tracking based on YOLO and ByteTrack. Proceedings of the 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Taizhou, China.10.1109/CISP-BMEI60920.2023.10373254 ↗
19. FishTrack: Multi-object tracking method for fish using spatiotemporal information fusion10.1016/j.eswa.2023.122194 ↗
20. Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M. (2023, January 18–22). 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, Vancouver, BC, Canada.10.1109/CVPR52729.2023.00721 ↗
21. Wu, D., Jiang, S., Zhao, E., Liu, Y., Zhu, H., Wang, W., and Wang, R. (2022). Detection of Camellia oleifera fruit in complex scenes by using YOLOv7 and data augmentation. Appl. Sci., 12.10.3390/app122211318 ↗
22. Qi, Y., He, Y., Qi, X., Zhang, Y., and Yang, G. (2023, January 1–6). Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision, Paris, France.10.1109/ICCV51070.2023.00558 ↗
23. Liu, R., Lehman, J., Molino, P., Petroski Such, F., Frank, E., Sergeev, A., and Yosinski, J. (2018, January 2–8). An intriguing failing of convolutional neural networks and the coordconv solution. Proceedings of the Neural Information Processing Systems, Montreal, QC, Canada.
24. Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., and Sun, J. (2021, January 20–25). Repvgg: Making vgg-style convnets great again. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.10.1109/CVPR46437.2021.01352 ↗
25. Dai, X., Chen, Y., Xiao, B., Chen, D., Liu, M., Yuan, L., and Zhang, L. (2021, January 20–25). Dynamic head: Unifying object detection heads with attentions. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.10.1109/CVPR46437.2021.00729 ↗
26. Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., and Wang, X. (2022, January 23–27). Bytetrack: Multi-object tracking by associating every detection box. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.10.1007/978-3-031-20047-2_1 ↗