基于改进YOLOv7的密集鱼群计数检测

Dense Fish Population Counting Detection Based on Improved YOLOv7

【目的】提高在水体浑浊和鱼群高密度聚集等复杂环境中的鱼群检测精度。【方法】提出一种基于双层路由注意力机制(BiFormer)和Normalized Wasserstein Distance(NWD)损失函数的改进YOLOv7的密集鱼群计数检测方法。在保留细粒度特征的基础上,提高模型对多尺度特征的学习能力,同时降低模型对模糊图像中小目标位置偏差的敏感性,加强对浑浊水域中鱼群的识别能力。为评估该模型的有效性,在红鳍东方鲀(Takifugu rubripes)数据集上与其他网络模型进行对比实验。【结果】该方法在红鳍东方鲀数据集上的准确率和召回率分别达到98.05%和97.69%,平均精度达到99.10%,较YOLOv7相比分别提升2.46%、3.73%和2.62%。与目前检测准确率较高的其他水下目标检测模型相比,平均精度平均提高4.25%。【结论】实现真实养殖环境下浑浊水域中鱼群的准确检测,有助于科学指导工业化水产养殖的生产管理,提高养殖效率,减少资源浪费。

【Objective】To improve the accuracy of fish school detection in complex environments such as turbid water bodies and high-density clustering of fish school.【Method】Dense fish population count detection method by an improved YOLOv7 is proposed,based on Vision Transformer with Bi-Level Routing Attention(BiFormer)and the Normalized Wasserstein Distance(NWD)loss function.On the basis of retaining fine-grained features,the model’s ability to learn multi-scale features was improved,and the sensitivity of the model to the location deviation of small targets in fuzzy images was reduced,and the ability to identify fish in turbid waters was strengthened.In order to evaluate the effectiveness of the proposed model,comparative experiments with other network models were carried out on the dataset of the pond-cultured Takifugu rubripes.【Result】Comprehensive experimental results demonstrate that the precision and recall rate of the proposed method on the Takifugu rubripes dataset reach 98.05%and 97.69%respectively,and the average precision reaches 99.1%,which are 2.46%,3.73%and 2.62%higher than those of YOLOv7.The proposed model is also compared with current underwater target detection models with high detection accuracy.The average precision of the proposed model is increased by 4.25%on average.【Conclusion】The accurate detection of fish in turbid waters within real-world aquaculture environments is pivotal in guiding industrial aquaculture production and management with greater scientific precision.This advancement not only enhances aquaculture efficiency but also minimizes resource waste,thereby promoting sustainable aquaculture development.