基于改进YOLOv5的水产养殖细菌性鱼病病原细菌检测算法

Detection algorithm of pathogenic bacteria of aquaculture bacterial fish disease based on improved YOLOv5

在水产养殖中,鱼病病原细菌的增长会暴发细菌性鱼病,引发大量养殖鱼类的死亡。因此对细菌性鱼病病原细菌质量浓度的监测极其重要。为了能够快速准确地统计出鱼病病原细菌这类小目标的数量和质量浓度,将深度学习引入水产养殖中,提出一种基于YOLOv5的鱼病病原细菌检测改进算法。首先在路径聚合网络结构中增加一层自下而上的路径增强结构,并且和主干特征提取网络中的第一层CSP模块输出的特征图进行特征融合,提高鱼病病原细菌的检测精度。然后在主干特征提取网络中的每一个卷积模块后添加注意力机制,对卷积模块提取到的特征进一步细化。最后,针对鱼病病原细菌数据集利用K-means++聚类算法获得和特征图更加匹配的先验框。结果显示,相比于原始YOLOv5算法,改进后的算法在测试集上的平均准确率均值为69.19%,提高了2.34%,验证了增加上采样层和注意力机制对鱼病病原细菌这类小目标的检测具有很好的效果。该方法可扩展应用于鱼虾卵的检测和识别上,根据鱼虾卵的数量供应匹配的饲料和氧气等,具有广泛应用前景。

In aquaculture,the growth of fish pathogenic bacteria can cause outbreaks of bacterial fish diseases and trigger the death of a large number of cultured fish.Therefore,it is extremely important to monitor the concentration of bacterial fish disease pathogenic bacteria.To count the number and concentration of small targets quickly and accurately such as fish disease pathogenic bacteria,deep learning was introduced into aquaculture,and an improved detection algorithm for fish disease pathogenic bacteria based on YOLOv5 was proposed.First,a layer of bottom-up path enhancement is added to the path aggregation network structure,and feature fusion is performed with the feature map output from the first CSP module in the backbone feature extraction network to improve the accuracy of fish pathogenic bacteria detection.Then the attention mechanism is added after each convolutional module in the backbone feature extracting network to further refine the features extracted by the convolutional module.Finally,the K-means clustering algorithm is used for the fish pathogenic bacteria dataset to obtain a priori frames that match more closely with the feature maps.It is shown that the mean average accuracy of the improved algorithm on the test set is 69.19%,an improvement of 2.34%,It is verified that adding an upsampling layer and an attention mechanism has a good effect on the detection of small targets such as fish pathogenic bacteria.It is shown that the improved algorithm in this paper can accurately detect bacteria,effectively improving the detection accuracy of fish-pathogenic bacteria and reducing the rate of missed and wrong detection.The method can also be applied to the detection and identification of fish and shrimp eggs by supplying feed and oxygen matched to the number of eggs,etc.