Automating Fish Detection and Species Classification in Underwaters Using Deep Learning Model
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-2742-5_39
Reference15 articles.
1. Knausgård KM, Wiklund A, Sørdalen TK et al (2022) Temperate fish detection and classification: a deep learning based approach. Appl Intell 52:6988–7001. https://doi.org/10.1007/s10489-020-02154-9
2. Al Muksit A, Hasan F, Emon MFHB, Haque MR, Anwary AR, Shatabda S (2022) YOLO-Fish: a robust fish detection model to detect fish in realistic underwater environment. Ecol Inform 72:101847. ISSN 1574-9541
3. Mohamed HED, Fadl A, Anas O, Wageeh Y, ElMasry N, Nabil A, Atia A (2020) MSR-YOLO: method to enhance fish detection and tracking in fish farms. Procedia Comp Sci 170:539–546. ISSN 1877-509
4. Pagire V, Phadke A (2022) Underwater fish detection and classification using deep learning. In: 2022 International conference on intelligent controller and computing for smart power (ICICCSP), Hyderabad, India, 2022, pp 1–4. https://doi.org/10.1109/ICICCSP53532.2022.9862410
5. https://www.sciencedaily.com/releases/2021/02/210217132320.htm
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