论文标题
渔网：统一的鲑鱼识别
FishNet: A Unified Embedding for Salmon Recognition
论文作者
论文摘要
识别单个鲑鱼对水产养殖行业非常有益，因为它可以监测和分析鱼类的行为和福利。对于水产养殖研究人员而言，识别单个鲑鱼的研究对他们的研究至关重要。当前单个鲑鱼标签和跟踪的方法取决于与鱼类的物理互动。这个过程效率低下，会对鲑鱼造成身体伤害和压力。在本文中，我们根据已成功用于识别人类的深度学习技术提出了渔网，以识别鲑鱼。我们创建一个标记的鱼类图像的数据集，然后测试渔网建筑的性能。我们的实验表明，该体系结构根据鲑鱼头的图像学习了有用的表示形式。此外，我们表明，通过相对较小的神经网络模型可以实现良好的性能：鱼网的假阳性率为1 \％，而真正的正率为96 \％。
Identifying individual salmon can be very beneficial for the aquaculture industry as it enables monitoring and analyzing fish behavior and welfare. For aquaculture researchers identifying individual salmon is imperative to their research. The current methods of individual salmon tagging and tracking rely on physical interaction with the fish. This process is inefficient and can cause physical harm and stress for the salmon. In this paper we propose FishNet, based on a deep learning technique that has been successfully used for identifying humans, to identify salmon.We create a dataset of labeled fish images and then test the performance of the FishNet architecture. Our experiments show that this architecture learns a useful representation based on images of salmon heads. Further, we show that good performance can be achieved with relatively small neural network models: FishNet achieves a false positive rate of 1\% and a true positive rate of 96\%.
