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A Novel fish counting Method Based on Multiscale and Multicolumn Convolution Group Network

国际计算机前沿大会会议论文集 2022年第1期 339-353页

作者： Yuxuan Zhang Junfeng Wu Hong Yu Shihao Guo Yizhi Zhou Jing Li School of Information Engineering Dalian Ocean UniversityDalian 116023China Key Laboratory of Environment Controlled Aquaculture(Dalian Ocean University)Ministry of Education Dalian 116023China

An accurate grasp of the number of fish in the breeding pond or fixed waters can provide an important basis for bait placement and reasonable fishing,and these data can also provide the necessary data support for accurate *** to the high density of fish in the real underwater environment,the strong occlusion and the large amount of adhesion,it is difficult to count fish,and the accuracy is *** the above issues,we present a new approach to a fish counting method based on a multiscale multicolumn convolution group *** enhance the counting accuracy and reduce the complexity of the network,this method uses an asymmetric convolution kernel to change the traditional convolution kernel,which increases our network depth and appreciably reduces the size of the *** the backbone network,a convolutional group is used to replace a single convolutional layer to enhance the learning capacity of the *** back of the net introduces the spatial structure of the pyramid and the multicolumn dilated convolution,which preserves the different scaling properties of fish data and improves the capabilities of the fish counting *** check the performance of the algorithm,this work collects and labels the DLOU3 fish dataset suitable for counting fish and conducts simulation experiments on the DLOU3 fish dataset using our *** experiments are comparedwith other popular fish counting algorithms in terms of the mean absolute error(MAE)and mean square error(MSE).The MAE and MSE of the final experimental results of our method are 5.36 and 6.56 and 23.67 and 32.52 in the two test sets,respectively,and the best performance among the five groups of algorithms is obtained.

关键词： fish counting Neural networks Asymmetric convolution Dilated convolution

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fish Density Estimation with Multi-Scale Context Enhanced Convolutional Neural Network

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Journal of Communications and Information Networks 2019年第3期4卷 80-88页

作者： Yizhi Zhou Hong Yu Junfeng Wu Zhen Cui Hongshuai Pang Fangyan Zhang College of Information Engineering Dalian Ocean UniversityDalian 116023China Key Laboratory of Facility fisheries Ministry of EducationDalian116023China

With the development of fishery industry,accurate estimation of the number of fish in aquaculture waters is of great importance to fish behavior analysis,bait feeding and fishery resource *** this paper,we propose a method for fish density estimation based on the multi-scale context enhanced convolutional network,which could map a fish school image taken at any angle to a density map,and calculate the number of fish in the image *** order to eliminate the influence of camera perspective effect and image resolution on density estimation,multi-scale filters are utilized in a convolutional neural network to process fish image in *** then,the context enhancement module is merged in the network structure to help the network understand the global context information of the ***,different feature maps are merged together to construct the density map of fish school images,and finally get the number of fish in the *** order to make the effectiveness of our method valid,we test the proposed method on *** results show that the proposed method has lower mean square error and mean absolute error,which is helpful to improve the accuracy of the fish counting in dense fish school images.

关键词： fish counting density estimation neural network context enhancement module

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Using image processing technology to create a novel fry counting algorithm

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Aquaculture and fisheries 2022年第4期7卷 441-449页

作者： Jianfei Zhang Haitong Pang Weiming Cai Zhonghong Yan School of Information Science and Engineering NingboTech UniversityNingbo315100China College of Control Science and Engineering Zhejiang UniversityHangzhou310058China Chongqing University of Technology Banan400054China

Quantitative counting of fry is required for multiple reasons,including breeding,transportation,and sales and creating a fish-counting algorithm using image processing technology is proposed as a useful *** images for fish fry were preprocessed to extract the details of the target fish,using methods including binarization,dilation,and *** thinning and the connected area algorithms were then independently adopted to count the fish fry within the *** advantages and the disadvantages of the two algorithms under different densities were also compared,revealing that the fish fry pictures with a clear contrast between the foreground and background resulted in a higher *** main purpose of this research is to solve the problem of fry counting in the case of high density and high *** research provides a reference point for this method based on image technology.

关键词： fish counting Image recognition Thinning algorithm Connected area algorithm

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