Zhang Song, Yang Xinting, Wang Yizhong, Zhao Zhenxi, Liu Jintao, Liu Yang, Sun Chuanheng, Zhou Chao
College of Electronic Information and Automation, Tianjin University of Science and Technology, Tianjin 300222, China.
Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China.
Animals (Basel). 2020 Feb 24;10(2):364. doi: 10.3390/ani10020364.
In intensive aquaculture, the number of fish in a shoal can provide valuable input for the development of intelligent production management systems. However, the traditional artificial sampling method is not only time consuming and laborious, but also may put pressure on the fish. To solve the above problems, this paper proposes an automatic fish counting method based on a hybrid neural network model to realize the real-time, accurate, objective, and lossless counting of fish population in far offshore salmon mariculture. A multi-column convolution neural network (MCNN) is used as the front end to capture the feature information of different receptive fields. Convolution kernels of different sizes are used to adapt to the changes in angle, shape, and size caused by the motion of fish. Simultaneously, a wider and deeper dilated convolution neural network (DCNN) is used as the back end to reduce the loss of spatial structure information during network transmission. Finally, a hybrid neural network model is constructed. The experimental results show that the counting accuracy of the proposed hybrid neural network model is up to 95.06%, and the Pearson correlation coefficient between the estimation and the ground truth is 0.99. Compared with CNN- and MCNN-based methods, the accuracy and other evaluation indices are also improved. Therefore, the proposed method can provide an essential reference for feeding and other breeding operations.
在集约化水产养殖中，鱼群数量可为智能生产管理系统的开发提供有价值的输入。然而，传统的人工采样方法不仅耗时费力，而且可能给鱼带来压力。为解决上述问题，本文提出一种基于混合神经网络模型的自动鱼类计数方法，以实现远海三文鱼养殖中鱼群数量的实时、准确、客观和无损计数。使用多列卷积神经网络（MCNN）作为前端来捕捉不同感受野的特征信息。采用不同大小的卷积核来适应鱼的运动所引起的角度、形状和大小的变化。同时，使用更宽更深的扩张卷积神经网络（DCNN）作为后端，以减少网络传输过程中空间结构信息的损失。最后，构建了混合神经网络模型。实验结果表明，所提出的混合神经网络模型的计数准确率高达[95.06%]，估计值与真实值之间的皮尔逊相关系数为[0.99]。与基于卷积神经网络（CNN）和MCNN的方法相比，准确率和其他评估指标也有所提高。因此，所提出的方法可为投喂及其他养殖操作提供重要参考。
