Abstract:  Aiming at the problem that the traditional deep learning fish recognition method has a low accuracy rate and the parameters cannot be determined adaptively during the model training process. This paper proposes an improved ShuffleNet fish identification method based on Particle Swarm Optimization (PSO) and Transfer Learning (TL). The research takes 20 species of fish as the object, uses the particle swarm algorithm to take the model's loss function as the fitness function, optimizes the two hyperparameters of batch size and learning rate, and uses the transfer learning method for training to construct TL-PSO-ShuffleNet model. The research shows that compared with the models of AlexNet, MobileNet, and ShuffleNet, the recognition accuracy rate is increased by 57.89%, 30.43%, and 23.28%, respectively. The fish identification method proposed in this paper has the characteristics of high accuracy and self-adaptive parameter setting, which provides a reference for the research on automatic fish identification.

Key words: fish recognition, deep learning, convolutional neural networks, particle swarm optimization

摘要： 针对传统深度学习鱼类识别方法正确率较低、模型训练过程中参数不能够自适应确定的问题，提出了一种基于迁移学习（Transfer Learning，TL）的粒子群（Particle Swarm Optimization，PSO）改进ShuffleNet鱼类识别方法。以20种鱼类为对象，采用粒子群算法将模型的损失函数作为适应度函数，对批处理大小和学习率两个超参数进行优化，并利用迁移学习方式进行训练，构建了TL-PSO-ShuffleNet模型。结果显示：该方法与AlexNet、MobileNet、ShuffleNet模型相比，识别正确率分别提高了57.89%、30.43%、23.28%。本研究提出的鱼类识别方法具有正确率较高、参数设定具备自适应性等特点，为鱼类自动化识别研究提供了参考和借鉴。

关键词: 鱼类识别, 深度学习, 卷积神经网络, 粒子群优化