基于改进深度学习模型的鱼群密度检测试验研究

Experimental research on fish density detection based on improved deep learning model

在水产养殖生产中,鱼群密度的检测是做好生产管理的关键环节。基于水下鱼类的群聚现象,采用基于拥塞场景识别卷积神经网络(Congested Scene Recognition Convolutional Neural Networks,CSRNet)技术,将剔除了全连接层的VGG-16与空洞卷积神经网络相结合,保持分辨率的同时扩大感知域,从而生成高质量的鱼群分布密度图。结果显示:CSRNet在仿真鱼群数据集中,检测准确率达90%以上,预测密度图与真实情况接近,失真度小;在预测真实鱼群密度中也同样表现良好。CSRNet与传统的基于光栅图方法相比,准确率提升近10%;与同样基于VGG-16的Faster R-CNN相比,CSRNet的表现更为优越。研究表明,构建的检测系统软件可实时检测定点区域鱼群密度是否处于正常范围,有利于预防鱼群高密度缺氧,提高鱼量产出,实现智能养殖。

In the field of aquaculture production,fish density detection is a key link to production management.In view of the clustering of underwater fish,the technology based on Congested Scene Recognition Convolutional Neural Networks(CSRNet)is used to combine the VGG-16 without the full connection layer with the hollow convolutional neural network,which maintains the resolution and expand the perceptual domain,thus generating a high-quality map of the distribution density of fish.The results show that the detection accuracy of CSRNet in the simulated fish data set is over 90%.The predicted density map is similar to the real situation with a small distortion.It also performs well in predicting the real fish density.Compared with the traditional raster image-based method,CSRNet improves the accuracy by approximately 10%.In the meantime,CSRNet performs better than Faster R-CNN,which is also based on VGG-16.The research shows that the software of detection system constructed can detect in real time whether the fish density in the fixed point area is in the normal range.It is beneficial to prevent the phenomenon of high density hypoxia of fish,increase the output of fish,and achieve intelligent aquaculture.