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DOI: 10.14569/IJACSA.2020.0110202
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Fast and Accurate Fish Detection Design with Improved YOLO-v3 Model and Transfer Learning

Author 1: Kazim Raza
Author 2: Song Hong

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 2, 2020.

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Abstract: Object Detection is one of the problematic Computer Vision (CV) problems with countless applications. We proposed a real-time object detection algorithm based on Improved You Only Look Once version 3 (YOLOv3) for detecting fish. The demand for monitoring the marine ecosystem is increasing day by day for a vigorous automated system, which has been beneficial for all of the researchers in order to collect information about marine life. This proposed work mainly approached the CV technique to detect and classify marine life. In this paper, we proposed improved YOLOv3 by increasing detection scale from 3 to 4, apply k-means clustering to increase the anchor boxes, novel transfer learning technique, and improvement in loss function to improve the model performance. We performed object detection on four fish species custom datasets by applying YOLOv3 architecture. We got 87.56% mean Average Precision (mAP). Moreover, comparing to the experimental analysis of the original YOLOv3 model with the improved one, we observed the mAP increased from 87.17% to 91.30. It showed that improved version outperforms than the original YOLOv3 model.

Keywords: Deep learning; computer vision; transfer learning; improved YOLOv3; anchor box; custom dataset

Kazim Raza and Song Hong, “Fast and Accurate Fish Detection Design with Improved YOLO-v3 Model and Transfer Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 11(2), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110202

@article{Raza2020,
title = {Fast and Accurate Fish Detection Design with Improved YOLO-v3 Model and Transfer Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110202},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110202},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {2},
author = {Kazim Raza and Song Hong}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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