Virtual Measurement Of Eriocheir Sinensis Based On Image Segmentation And Support Vector Regression

The Eriocheir sinensis is a dominant aquaculture industry in China.With the improvement of people’s living standards and the popularity of crab eating culture,the demand for Eriocheir sinensis is increasing,and Eriocheir sinensis has become one of the main aquaculture objects in China.One of the important issues in the process of Eriocheir sinensis cultivation is to accurately and continuously monitor the body size and weight information of Eriocheir sinensis,to assess the health status of Eriocheir sinensis and optimize daily feeding processes,providing scientific guidance for determining the optimal fishing time.However,the conventional measurement methods are manual measurements.By taking a small amount of samples from water for measurement,the labor cost is high and the accuracy is low,which can lead to stress reactions in the Eriocheir sinensis and affect its growth.Therefore,designing a low-cost,contactless,high-precision,and generalized method to achieve automated measurement of Eriocheir sinensis is of great significance to the aquaculture industry.In this paper,the Yangtze River No.1 Eriocheir sinensis was taken as the research object,and the feasibility of a contactless rapid estimation method for Eriocheir sinensis was explored using image segmentation techniques and support vector machine regression methods.A method for body size measurement and weight estimation of Eriocheir sinensis based on improved Yolov5-Seg and SSA-SVR was designed.The main research contents of this article are as follows:(1)The measurement of the body size of Eriocheir sinensis is the measurement of the length and width of the carapace.The measurement method of Eriocheir sinensis body size based on computer vision first requires obtaining accurate head and chest armor contours.This paper proposes an improved semantic segmentation method based on Yolov5-Seg,which uses the GCBlock module in GCNet to reconstruct the C3 module in the backbone network of Yolov5-Seg,capturing the remote dependencies between pixels,and improving the feature extraction ability of the model.In Yolov5-Seg,the Neck side uses a new GSConv convolution method to reduce the computational complexity of the model.This method has been tested on two open semantic segmentation datasets,DUTUSEG and Trashcan,and has successfully applied the model to the Eriocheir sinensis dataset.On the DUT-USEG,Trashcan,and self built Eriocheir sinensis datasets,the m AP has been increased by 2.2%,1.8%,and 3.5%,respectively.Using the C3 GS module instead of the C3 module on the feature fusion Neck side can reduce the computational complexity by 5.6%.The experimental results show that this method can effectively increase the learning of global features by neural networks,reduce the computational complexity of the model,and improve the accuracy of the Yolov5-Seg network on various data sets.Through verification with different types of data sets,it is proved that its generalization is also excellent.This model effectively solves the problem of inaccurate image segmentation results in semantic segmentation networks.(2)The regression equations for the body length,body width,projected area of the head and chest armor,and body weight of Eriocheir sinensis were established using the support vector machine regression SSA-SVR method optimized by the sparrow search algorithm.The calculation process of the method in this article: Firstly,the improved Yolov5-Seg semantic segmentation method is used to obtain the image segmentation results of Eriocheir sinensis,and preserve the contour information of the head and chest armor of Eriocheir sinensis.Secondly,the average depth information of Eriocheir sinensis was calculated using contour point coordinates and depth maps,and the body size information and the projected area of the head and chest armor of Eriocheir sinensis were calculated using contour information and depth information.Finally,using the calculated body size information and the projected area of the head and breastplate of Eriocheir sinensis as inputs,the weight information of Eriocheir sinensis was estimated based on the SSA-SVR regression equation.The calculation results show that the average relative error of body length of Eriocheir sinensis is 5.55%,the average absolute error is0.17 cm,and the root mean square error is 0.18 cm.The average relative error of body width of Eriocheir sinensis is only 2.98%,with an average absolute error of 0.11 cm and a root mean square error of 0.11 cm.The average relative error of body weight estimation of Eriocheir sinensis is 9.31%,with an average absolute error of 2.18 g and a root mean square error of 2.24 g.The experimental results show that this method can not only maintain good measurement accuracy,but also have advantages such as low deployment equipment requirements,and stronger generalization.The research results provide a theoretical basis for non-contact body size measurement and weight estimation research of Eriocheir sinensis.Finally,this article also makes a prospect for the future research direction of this method.