A residual bait counting method based on computer vision

Abstract

The invention relates to a residual bait counting method based on computer vision. Compared with the prior art, the method comprises the following steps: 1) obtaining an underwater image, and preprocessing the image by adopting an image clarification algorithm to blur the image and lose details caused by the turbidity of the pool water; 2) considering the illumination unevenness caused by artificial light source, segmenting the foreground and background by adaptive classification and segmentation algorithm and converting it into binary image; 3) searching the contour sequence of the foregroundobject, and eliminating contours whose number of contour points does not belong to the set range; 4) removing the contour of the non-food object based on the contour feature of the foreground object;5) calculating that area of each contour after the reject treatment and sorting the area to determine the area of a single bait; 6) estimating that number of bait particle contained in the current contour and adding the estimated number of particle of all contours to obtain the final residual bait particles. The present invention has the advantages of adapting to turbidity pond water environment,adapting to uneven illumination, considering adhesion, accurate counting and the like.

Description

technical field [0001] The invention relates to the fields of aquaculture and image processing, in particular to a computer vision-based residual bait counting method. Background technique [0002] Feed waste has always been a serious problem in aquaculture. On the one hand, because bait accounts for a large proportion in the economic cost of aquaculture, the remaining bait is a huge economic loss. On the other hand, the remaining bait will pollute the water source, deteriorate the growth environment of the cultured organisms, and increase the disease risk of the cultured organisms. Therefore, accurate acquisition of the remaining bait has very important practical significance for realizing accurate bait casting and computer vision. [0003] With the rapid development of theories and technologies such as digital image processing and waterproof cameras, more and more computer vision technologies are used in various fields of aquaculture, such as size measurement of aquacult...

AI Technical Summary

Problems solved by technology

[0004] It can be known from the existing research that many researchers have used computer vision technology to detect residual bait and achieved some results. However, many algorithms do not take into account the common conditions in the breeding environment such as turbidity of pool water, uneven illumination, and bait adhesion. The accuracy is low, it is only in the stage of theoretical research and cannot be used in actual production and cannot create economic benefits

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