鱼类目标检测对渔业精准养殖、生产自动化、资源调查及鱼行为的研究等具有重要的意义。为了能快速准确地得到鱼类目标的位置和所属类别，提出了一种改进YOLO v4模型的鱼类目标检测方法，在CIoU（Complete Intersection over Union）损失函数基础上构建了新的损失项，改进的损失函数使真实框与相交框呈相同宽高比进行回归，同时通过设置多锚点框模式，增强在特定尺寸面积上的检测效果。结果显示：改进YOLO v4模型的mAP(mean Average Precision)比原模型有较大提升，在自建数据集、Fish4Knowledge数据集和NCFM数据集上的mAP分别达到了94.22%、99.52%、92.16%。研究表明，改进YOLO v4模型可以快速准确地检测到鱼的位置和类别，检测速度满足实时的要求，可以为渔业精准养殖等提供参考。

Fish object detection is of great significance for precision aquaculture, production automation, resource investigation and fish behavior research. In order to get the position and category of fish object quickly and accurately, a fish object detection method based on improved model YOLO V4 is proposed, Based on the CIoU(complete intersection over union) loss function, a new loss term is constructed. The improved loss function makes the real box and the intersecting box regress in the same aspect ratio. At the same time, the detection effect on a specific size and area is enhanced by setting a multi anchor box mode. The results show that the mAP (mean average precision) of the improved model YOLO V4 is greatly improved compared with the original model. The mAP on the self built data set, data set Fish4 knowledge and data set NCFM reaches 94.22%, 99.52% and 92.16% respectively. The research shows that the improved model YOLO v4 can quickly and accurately detect the position and category of fish, and the detection speed meets the real-time requirements, which can provide a reference for precision aquaculture of fishery.