A Novel Approach to Automatic Identification and Detection of Aquatic Animal Species
Marine fisheries contribute greatly to the economic aspects of any country. India, having a coastline of almost 8000 KM, a surplus of fisheries potential could be estimated here. Because of this vast coastal area, active reporting of captured fishes is difficult through manual monitoring. Computer-aided approach is the best suitable option during the active season. This paper focuses on investigating an approach for identifying single as well as multiple aquatic animal species in a single image. Further a responsive web as well as mobile application are developed, in which the ML models are integrated. This will help users to access data as per their use. The method used YOLOv5n, a lightweight object detection algorithm, to detect these species. The trained model yielded [email protected]:0.95 intersection over union (IoU), and average precision (AP) for each species. The species’ AP varied as well. Few GFLOPs are used by YOLOv5n. This indicates that it is a scaled-down version capable of running on the 5.1 GFLOP Raspberry Pi 3B+. Despite employing substantially fewer GFLOPs, YOLOv5n outperformed Faster R-CNN.
This work is licensed under a Creative Commons Attribution 4.0 International License.
- G, M., J. R. . S. K. S. . . B. D. . Developing machine learning based framework for the network traffic prediction: Machine learning based traffic prediction. International Journal of Next-Generation Computing, 13(3), (2022). DOI: https://doi.org/10.47164/ijngc.v13i3.787
- J, D., . T. L. Digital decision making in dentistry: Analysis and prediction of periodontitis using machine learning approach. International Journal of Next-Generation Computing, 13(3), (2022). DOI: https://doi.org/10.47164/ijngc.v13i3.614
- Jocher, G. e. a. yolov5. code repository https://github. com/ultralytics/yolov5. (2020). International Journal of Applied Mathematics Electronics and Computers .
- John Alfred J. Casta˜neda1, Angelo L. De, C. M. A. G. S. N. A. M. J. T. T. and Karim, H. A. Development of a detection system for endangered mammals in negros island, philippines using yolov5n. International Journal of Applied Mathematics Electronics and Computers.
- Lyes, D., Leila, F., and Hocine, T. 2014. Interface for dynamic modification of the transformation parameters of the psola algorithm. International Journal of Applied Mathematics Electronics and Computers 2, 4, 26 – 30. DOI: https://doi.org/10.18100/ijamec.90459
- M. Guillaumin, J. V. and Schmid, C. Multimodal semi-supervised learning for image classification. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Nguyen, N.-D. e. a. An evaluation of deep learning methods for small object detection. Journal of Electrical and Computer Engineering. 2020, (2020). DOI: https://doi.org/10.1109/CVPR.2010.5540120
- Nicolas Audebert, Catherine Herold, K. S. . C. V. Multimodal deep networks for text and image-based document classification. https://link.springer.com/chapter/10.1007/978- 3-030-43823-435(2020)..
- Nwankpa, C. e. a. Activation functions: Comparison of trends in practice and research for deep learning. arXiv preprint arXiv:1811.03378. (2018).
- S. Bahrampour, N. M. Nasrabadi, A. R. and Jenkins, W. K. 2014. Multimodal taskdriven dictionary learning for image classification. IEEE Transactions on Image Processing DOI: https://doi.org/10.1109/ICASSP.2015.7178185
- vol. 25, no. 1, pp. 24-38, Jan. 2016. DOI: https://doi.org/10.1097/01.COT.0000511606.84427.7b
- Tzutalin, D. 2014. Labelimg. GitHub Repository. 6, (2015).
- Wang, C.-Y. e. a. A new backbone that can enhance learning capability of cnn. Proceedings
- of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp.
- –391 (2020).
- Zhang, Z., S. M. Generalized cross entropy loss for training deep neural networks with noisy
- labels. Advances in neural information processing systems. 31, (2018)..