基于改进的卷积神经网络的虾苗自动计数研究

Research on automatic counting of shrimp fry based on improved convolutional neural network

虾苗计数是虾苗养殖与交易过程的重要环节。因虾苗个体小、密度大、易失活等特点,导致虾苗出苗计数非常困难。提出了一种卷积神经网络模型的虾苗自动计数技术,先将数据样本中每个目标对象用一个像素的点标注获取真实密度图,再将训练样本输入到改进卷积神经网络学习以便从图像特征生成估计密度图,最后由密度图获得整个视野中的虾苗总数。为验证方法的有效性,以虾苗场拍摄的虾苗图像做数据集并在不同模型上进行对比试验。结果显示:与多列卷积神经网络(MCNN)、拥挤场景识别网络(CSRNet)、上下文感知网络(CAN)等经典网络相比,其平均绝对误差可分别减少7.6、4.8、3.2。研究表明,该方法在均匀背光环境下能够对一定密度的虾苗准确估计其数量,符合虾苗养殖业的计数要求。

Shrimp fry counting is an important part of the shrimp fry breeding and transaction process.Shrimp fries are small,dense and easily inactivated,which makes it difficult to count shrimp fry upon emergence.An automatic counting technology for shrimp fry with a convolutional neural network model is proposed.First,each target object in the data sample is labeled with a pixel point to obtain the true density map,then the training samples are input to the improved convolutional neural network learning to generate an estimated density map from the image features,and finally the total number of shrimp fry in the entire field of view is obtained from the density map.In order to verify the effectiveness of the method,the shrimp fry images taken in the fry farm are used as data sets and comparative experiments are conducted on different models.The results show that:compared with the classical networks such as MCNN,CSRnet and CAN,the average absolute error can be reduced by 7.6,4.8 and 3.2 respectively.Researches have shown that this method can accurately estimate the number of shrimp fry of a certain density under a uniform backlight environment,which meets the counting requirements of shrimp aquaculture.