基于ResNet-CA的鱼群饱腹程度识别方法

Identification Method of Fish Satiation Level Based on ResNet-CA

投喂作为水产养殖过程中的一个关键环节,饵料的投喂量直接影响水产品的质量和养殖成本。然而,目前的投喂方法包括人工投喂和机器定时定量投喂,大多依靠人工经验,很难实现精准投喂。本文基于改进的ResNet34识别鱼群不同的饱腹程度。根据鱼群在不同饱腹阶段表现的摄食行为创建了含有5种不同饱腹程度的数据集,并采用数据增强操作对图像进行预处理。其次在原始模型ResNet34的基础上,本文提出使用坐标注意力机制,使模型在对图像进行特征提取的过程中能够做到专注于更大区域范围。并且使用深度可分离卷积的方式来代替传统卷积,减少模型参数量。为了评估改进的有效性,分析了改进后的模型在鱼群饱腹程度数据集上的性能,并将其与原模型ResNet34、AlexNet、VGG16、MobileNet-v2、GoogLeNet等经典卷积神经网络架构进行比较。综合实验结果表明,该模型相较于原模型参数量减少46.7%,准确率达到93.4%,相较于原模型提升3.4个百分点,同时改进后的模型在准确率、精确度、召回率等方面也都优于其他卷积神经网络。综上所述,本模型实现了性能与参数量之间的良好平衡,为后续模型在实际养殖环境中的部署并指导养殖户改善和制定投喂策略提供了可能。

Feeding as a key part of the aquaculture process,the amount of bait fed directly affects the quality of aquatic products and the cost of aquaculture.However,the current feeding methods include manual feeding and machine feeding at regular intervals,which mostly rely on manual experience and are difficult to achieve accurate feeding.Different satiation levels of fish were identified based on the improved ResNet34,which was important for achieving accurate control of bait feeding in the future.A dataset containing five different satiation levels was created based on the feeding behaviors exhibited by fish at different satiation stages,and the images were pre-processed using data enhancement operations.Secondly,based on the original model ResNet34,the use of coordinate attention mechanism wasproposed to enable the model to focus on a large area in the process of feature extraction of images.And the depth-separable convolution was used instead of the traditional convolution to reduce the number of model parameters.To evaluate the effectiveness of the improvements,the performance of the improved model wasanalyzed on the fish satiation dataset and compared it with the original model ResNet34,AlexNet,VGG16,MobileNet-v2,GoogLeNet and other classical convolutional neural network architectures.The comprehensive experimental results showed that the model reduced the amount of parameters by 46.7%and achieved an accuracy of 93.4%compared with the original model,which had a 3.4 percentage points improvement compared with the original model,and the improved model also outperformed other convolutional neural networks in terms of accuracy,precision,recall,and F1 score.In summary,the model achieved a good balance between performance and number of participants,which provided the possibility for subsequent models to be deployed in real farming environments and guide farmers in improving and developing feeding strategies.