摘要： 实时检测并获取养殖鱼群的健康状态是规模化渔业养殖实现精准、绿色养殖和可持续发展的关键技术之一，其中实时识别病死鱼并及时收集处理更是减轻养殖水域污染、防止病害扩散、降低养殖风险的有效举措。然而在复杂的浅滩环境中，如光照变化、目标重叠、位置不稳定以及水雾造成模糊，使病死金鲳鱼实时识别并收集非常具有挑战性。本研究提出一种基于YOLOv4-v1的改进算法，在PANet模块中集成自定义Super网络，对输入的特征图进行编码解码过程，在细粒度特征提取中减少外界环境带来的干扰。此外，利用tanh-v1函数激活，增强了特征传播并确保网络中最大信息流。同时采用Resblockbody1模块，提高了目标框的定位精度。在浅滩养殖场景中，分析病死金鲳鱼图像在不同模型上对比试验结果中，YOLOv4-v1网络识别病死金鲳鱼的（平均精度）值高达98.31%，实时检测性能达到了27 FPS。通过与YOLOv4网络对比试验可得，YOLOv4-v1算法在线下试验中，检测速度基本与原网络持平，且值相较于YOLOv4提升了3.36%，召回率提升了2.54%，分数（精确率与召回率的平衡点）提升了0.56%。研究表明，YOLOv4-v1方法在死鱼识别方面具有良好的应用场景。

关键词: 病死金鲳鱼, 精准实时识别, YOLOv4-v1算法, 浅滩养殖场景

Abstract:  Real-time detection and acquisition of the health status of farmed fish is one of the key technologies for large-scale fish farming to achieve accurate, green and sustainable development, among which, real-time identification of sick and dead fish and timely collection and treatment can constrain aquaculture water contamination, prevent disease spreading and reduce breeding risks. However, in a complex shoal environment, such as changes in illumination, overlapping targets, unstable positions, and blurring caused by water fog, it is very challenging to identify and collect sick and dead golden pomfrets in time. In this paper, an improved algorithm based on YOLOv4 is proposed. A custom Super network is integrated in the PaNet module, and the input feature is encoded and decoded to reduce the interference caused by the external environment in the fine-grained feature extraction. In addition, the activation of the tanh-v1 function enhances feature propagation and ensures the maximum information flow in the network. The Resblockbody1 module is simultaneously used to improve the positioning accuracy of the target frame. In the shoal farming scene, by analyzing the images of dead golden pomfrets and comparing the test results on different models, the YOLOv4-v1 network identified an  value of dead golden pomfrets as high as 98.31%, and the real-time detection performance reached 27FPS. Through the comparison experiment with the YOLOv4 network, YOLOv4-v1 algorithm has a basically same detection speed to the original network in the offline experiment, while the value is increased by 3.36%, the R rate increased by 2.54%, and the score (the balance between precision and recall) enhanced by 0.56%. It can be seen that the YOLOv4-v1 method has a good application potential in dead fish identification.

Key words: Sick to death Trachitus ovatus, accurate real-time identification, YOLOv4-v1 algorithm, Shoal breeding scene