摘要
针对鱼类连续摄食行为较难识别与量化的问题,提出一种基于帧间光流特征和改进递归神经网络(Recurrent neural network, RNN)的草鱼摄食状态分类方法。首先利用偏振相机搭建户外池塘采样系统,采集不同偏振角度水面图像,并基于图像饱和度和亮度模型自动选择低反光角度图像,构建图像样本库;其次通过光流法提取帧间运动特征,并基于投饲机开关状态构建时间序列帧间特征样本集,然后利用样本集训练改进RNN分类网络。以上海市崇明区瑞钵水产养殖专业合作社的试验数据对该方法进行验证。结果表明,研究方法综合准确率为91%,召回率为92.2%,均优于传统的鱼类摄食行为识别方法。研究结果可为鱼类精准投喂技术研究提供参考。
The accurate identification of fish feeding behavior is of great significance to reduce feed waste, reduce water pollution and improve aquaculture benefits. Accurate classification of fish feeding status can provide necessary basic support for accurate identification and quantification of fish feeding behavior. Aiming at the technical needs of accurate classification of continuous feeding behavior of fish, we proposed a feeding state classification method of grass carp based on inter frame optical flow characteristics and improved RNN(recurrent neural network). Firstly, in order to reduce the impact of outdoor natural light conditions on imaging quality, an outdoor pond sampling system was built by using polarization camera to collect water surface images with different polarization angles. And the low reflection angle images were automatically selected based on the image saturation and brightness model, and the image sample database was constructed on this basis;Secondly, the image optical flow was extracted by LK(Lucas Kanade) method,and the two parameters of speed and rotation angle were selected to characterize the image optical flow characteristics.The image was evenly segmented, and the optical flow intensity of each image block was quantified by the optical flow pulsation intensity. On this basis, the image optical flow intensity description vector is constructed to effectively describe the global optical flow characteristics of the image based on local features;Then, based on the switching time length of the feeder, the video samples were segmented, and combined with the extracted inter frame optical flow features, the time series feature sample set is constructed. Finally, the sample set was used to train the improved RNN classification network, and the accuracy, recall and average F1 score were used to evaluate the effectiveness of the network.The method was verified by the experimental data of Ruibo aquaculture professional cooperative in Chongming District, Shanghai. The results show that the average accuracy of this method is 91%, the average recall rate is 92.2%, and the average F1 score is 91.6, which are better than the traditional fish feeding behavior recognition methods. The results can provide a reference for the research of fish precision feeding technology.
作者
刘世晶
涂雪滢
钱程
周捷
陈军
LIU Shi-Jing;TU Xue-Ying;QIAN Cheng;ZHOU Jie;CHEN Jun(Fishery Machinery and Instrument Research Institute,Chinese Academy of Fishery Sciences,Shanghai 200092,China;Shanghai Chongming District Aquatic Technology Promotion Station,Shanghai 202150,China)
出处
《水生生物学报》
CAS
CSCD
北大核心
2022年第6期914-921,共8页
Acta Hydrobiologica Sinica
基金
上海市科技兴农项目(沪农科推字(2019)第3-2号)资助。
关键词
鱼类摄食
图像分类
光流法
RNN
偏振
Fish feeding
Image classification
Optical flow
RNN
Polarization
