Research on identification and counting method of turbot fry based on ResNet34 model
The accurate statistics of the number of fry plays an important role in improving the intelligent level of fry quality evaluation,breeding density estimation and fry sales.At present,the counting of Turbot seedling mainly relies on manual labor,which is of high labor intensity and low precision.Because of the characteristics of turbot fry,such as small individual,high transparency and irregular shape,the traditional method of seed counting cannot be directly applied to Turbot seed counting.To solve the above problems,a method of identifying and counting turbot fry based on ResNet34 was proposed.Firstly,a set of image sampling device was designed,which consisted of camera,light source and container,etc.,and was suitable for small target counting.The clear fry image was obtained by adjusting the Angle of camera and light source,and then image preprocessing methods such as background difference,Gaussian filter,global gray linear transformation and morphological processing were used to achieve the foreground segmentation and preliminary positioning of fry.In order to effectively unify the sample space and the target space to be identified,the minimum external moment is used to regularize the foreground image initially,and the regularized sample is used to build the image sample set.In the identification stage of turbot fry,the same pretreatment method was used to obtain the target region to be identified,and ResNet34 framework was introduced as the identification model to realize the accurate identification of the target region seed.Finally,the number of turbot seedlings was calculated by counting the number of targets to be identified.The experimental results showed that the method achieved good accuracy in the identification and counting of tiny fry.The average accuracy of the recognition of turbot fry using ResNet34 framework reached 94.27%,which was 8.64 percentage points higher than that of SVM method and AlexNet method,and 7.4 percentage points higher than that of SVM method.It is better than ResNet18(identification accuracy 93.21%)and ResNet50(identification accuracy 93.83%)and other similar structures.The average accuracy rate of fry counting of the model in this paper is 96.28%,indicating that the proposed sample set construction and identification method can meet the needs of small target counting,and can provide technical reference for fish fry counting.
万方数据
维普
