Publications by authors named "Chuanheng Sun"

2 Publications

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Automatic Fish Population Counting by Machine Vision and a Hybrid Deep Neural Network Model.

Animals (Basel) 2020 Feb 24;10(2). Epub 2020 Feb 24.

Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China.

In intensive aquaculture, the number of fish in a shoal can provide valuable input for the development of intelligent production management systems. However, the traditional artificial sampling method is not only time consuming and laborious, but also may put pressure on the fish. To solve the above problems, this paper proposes an automatic fish counting method based on a hybrid neural network model to realize the real-time, accurate, objective, and lossless counting of fish population in far offshore salmon mariculture. A multi-column convolution neural network (MCNN) is used as the front end to capture the feature information of different receptive fields. Convolution kernels of different sizes are used to adapt to the changes in angle, shape, and size caused by the motion of fish. Simultaneously, a wider and deeper dilated convolution neural network (DCNN) is used as the back end to reduce the loss of spatial structure information during network transmission. Finally, a hybrid neural network model is constructed. The experimental results show that the counting accuracy of the proposed hybrid neural network model is up to 95.06%, and the Pearson correlation coefficient between the estimation and the ground truth is 0.99. Compared with CNN- and MCNN-based methods, the accuracy and other evaluation indices are also improved. Therefore, the proposed method can provide an essential reference for feeding and other breeding operations.
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http://dx.doi.org/10.3390/ani10020364DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7070656PMC
February 2020

An adaptive image enhancement method for a recirculating aquaculture system.

Sci Rep 2017 07 24;7(1):6243. Epub 2017 Jul 24.

Beijing Research Center for Information Technology in Agriculture, Beijing, 100097, China.

Due to the low and uneven illumination that is typical of a recirculating aquaculture system (RAS), visible and near infrared (NIR) images collected from RASs always have low brightness and contrast. To resolve this issue, this paper proposes an image enhancement method based on the Multi-Scale Retinex (MSR) algorithm and a greyscale nonlinear transformation. First, the images are processed using the MSR algorithm to eliminate the influence of low and uneven illumination. Then, the normalized incomplete Beta function is used to perform a greyscale nonlinear transformation. The function's optimal parameters (α and β) are automatically selected by the particle swarm optimization (PSO) algorithm based on an image contrast measurement function. This adaptive image enhancement method is compared with other classic enhancement methods. The results show that the proposed method greatly improves the image contrast and highlights dark areas, which is helpful during further analysis of these images.
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http://dx.doi.org/10.1038/s41598-017-06538-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5524723PMC
July 2017