An Application of Fish Detection Based on Eye Search with Artificial Vision and Artificial Neural Networks
Abstract
:1. Introduction
2. Materials and Methods
2.1. Image Filtering
2.2. Background Subtraction
2.3. Hough Eye Detection
2.4. Fish Detection Using a Feed-Forward Artificial Neural Network
3. Size Estimation
4. Results
- Intel Core i7-3770 CPU (4 cores, 8 threads)
- NVIDIA GT 640 graphics card
- 16 GB of 1600 MHz DDR3 RAM
- WDC WD10EZRX Hard Disk
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Appendix B
References
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Results | European Perch | Brown Trout | Atlantic Wreckfish |
---|---|---|---|
Avg. Measured Size (cm) | 8.8 | 6.5 | 92.6 |
Std. Dev. Measured Size (cm) | 0.8 | 0.8 | 8.4 |
Avg. Absolute Error (cm) | 0.6 | 0.9 | 6.5 |
Std. Dev. Absolute Error (cm) | 0.6 | 0.7 | 5.2 |
Avg. Relative Error | 0.07 | 0.12 | 0.07 |
Std. Dev. Relative Error | 0.09 | 0.09 | 0.06 |
True Positives | 620 | 600 | 182 |
Detected False Positives | 44 | 31 | 47 |
Precision | 0.93 | 0.95 | 0.74 |
False Positive Ratio | 0.07 | 0.05 | 0.26 |
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Rico-Díaz, Á.J.; Rabuñal, J.R.; Gestal, M.; Mures, O.A.; Puertas, J. An Application of Fish Detection Based on Eye Search with Artificial Vision and Artificial Neural Networks. Water 2020, 12, 3013. https://doi.org/10.3390/w12113013
Rico-Díaz ÁJ, Rabuñal JR, Gestal M, Mures OA, Puertas J. An Application of Fish Detection Based on Eye Search with Artificial Vision and Artificial Neural Networks. Water. 2020; 12(11):3013. https://doi.org/10.3390/w12113013
Chicago/Turabian StyleRico-Díaz, Ángel J., Juan R. Rabuñal, Marcos Gestal, Omar A. Mures, and Jerónimo Puertas. 2020. "An Application of Fish Detection Based on Eye Search with Artificial Vision and Artificial Neural Networks" Water 12, no. 11: 3013. https://doi.org/10.3390/w12113013