Research on Counting Algorithm of Residual Feeds in Aquaculture Based on Machine Vision

The waste of feed has always restricted the development of aquaculture. This paper presents an algorithm that can accurately obtain the residual information of feeds after a feeding event. With the purpose of applying the residual feed counting algorithm to the actual production, we focused on solving problems of counting feed pellets, such as turbid pond water, feed adhesion etc. We carry our experiments with different water turbidity levels and with feed adhesion, and some experiments have over 100 pellets. Experiments show that relative error can still be maintained at about 10% under the condition of turbid water and feed adhesion, which is much better than 20% obtained by other counting algorithm.

[1]  Ali M. Reza,et al.  Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement , 2004, J. VLSI Signal Process..

[2]  Xiaoou Tang,et al.  Single Image Haze Removal Using Dark Channel Prior , 2011 .

[3]  Jo Arve Alfredsen,et al.  A computer vision approach for detection and quantification of feed particles in marine fish farms , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[4]  R. J. Petrell,et al.  Control of feed dispensation in seacages using underwater video monitoring: effects on growth and food conversion , 1997 .

[5]  Lihong Xu,et al.  Detection and recognition of uneaten fish food pellets in aquaculture using image processing , 2015, International Conference on Graphic and Image Processing.

[6]  Shree K. Nayar,et al.  Vision and the Atmosphere , 2002, International Journal of Computer Vision.

[7]  Karel J. Zuiderveld,et al.  Contrast Limited Adaptive Histogram Equalization , 1994, Graphics Gems.

[8]  Y.Y. Schechner,et al.  Recovery of underwater visibility and structure by polarization analysis , 2005, IEEE Journal of Oceanic Engineering.

[9]  Manish Soni,et al.  Segmentation of Underwater Objects using CLAHE Enhancement and Thresholding with 3-class Fuzzy C-Means Clustering , 2014 .

[10]  Shi-Min Hu,et al.  Global contrast based salient region detection , 2011, CVPR 2011.

[11]  Keiichi Abe,et al.  Topological structural analysis of digitized binary images by border following , 1985, Comput. Vis. Graph. Image Process..

[12]  Raanan Fattal,et al.  Single image dehazing , 2008, ACM Trans. Graph..

[13]  Lizhong Xu,et al.  Visual-adaptation-mechanism based underwater object extraction , 2014 .

[14]  Rabab K. Ward,et al.  Detection and counting of uneaten food pellets in a sea cage using image analysis , 1995 .

[15]  Jian Sun,et al.  Guided Image Filtering , 2010, ECCV.