Abstract
Fishermen need to remove abnormal or dead fish for the prevention of viral infection. However, the identification of diseased fish is more ambiguous that the identification of dead ones, and detecting abnormal fish is hard even for experienced fishermen to do. Therefore, the automatic detection of abnormal fish is desirable. However, the detection of abnormal fish by Deep Learning needs a lot of image data both of healthy fish and abnormal ones. There are many image data of healthy fish, but image data of abnormal fish are scarce. In situations where a large amount of data is not available, Fine-Tuning is commonly used. However, preprocessing is important in Fine-Tuning. In this research, we verify appropriate preprocessing for detection of abnormal fish by Fine-Tuning. Appropriate preprocessing for Fine-Tuning using VGG16, which is used for image recognition, resulted in an increase in Recall by 12.5 points compared to conventional methods.
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This work was presented in part at the joint symposium of the 27th International Symposium on Artificial Life and Robotics, the 7th International Symposium on BioComplexity, and the 5th International Symposium on Swarm Behavior and Bio-Inspired Robotics (Online, January 25–27, 2022).
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Okawa, R., Iwasaki, N., Okamoto, K. et al. Detection of abnormal fish by image recognition using fine-tuning. Artif Life Robotics 28, 175–180 (2023). https://doi.org/10.1007/s10015-022-00824-0
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DOI: https://doi.org/10.1007/s10015-022-00824-0