Abstract
Fish is one of the most important food sources worldwide and for people in Nordic countries. For this reason, fish has been widely cultivated, but aquacultural fish are severely affected by lice, maturity, wounds, and other harmful factors typically part of agricultural fish, resulting in millions of fish deaths. Unfortunately, diagnosing injuries and wounds in live salmon fish is difficult. However, this study uses image-based machine learning approaches to present a wound detection technique for live farmed salmon fish. As part of this study, we present a new dataset of 3571 photos of injured and non-wounded fish from the Institute of Marine Research's genuine fish tank. We also propose a Convolutional Neural Network tailored for such wound detection with 20 convolutional and five subsequent dense layers. The model incorporates methods such as dropout, early halting, and Gaussian noise to avoid overfitting. Compared to the established VGG-16 and VGG-19 models, the proposed approaches have a validation accuracy of 96.22%. The model has low 0.0199 and 0.941 false positive and true positive rates, making it a good candidate for accurate live production.
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The authors are supported by the Norwegian Research Council HAVBRUK2 innovation project CreateView Project nr. 309784.
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Gupta, A., Bringsdal, E., Salbuvik, N., Knausgård, K.M., Goodwin, M. (2022). An Accurate Convolutional Neural Networks Approach to Wound Detection for Farmed Salmon. In: Iliadis, L., Jayne, C., Tefas, A., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2022. Communications in Computer and Information Science, vol 1600. Springer, Cham. https://doi.org/10.1007/978-3-031-08223-8_12
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