A method of counting residual bait based on computer vision

Abstract

The invention relates to a method for counting residual baits based on computer vision. Compared with the prior art, the method comprises the following steps: 1) Acquiring an underwater image, and using an image clarity algorithm to correct the blurred image and the loss of details caused by the turbidity of the pool water. Image preprocessing; 2) Considering the uneven illumination caused by artificial light sources, the adaptive classification and segmentation algorithm is used to segment the foreground and background, and converted into a binary image; 3) Search for the contour sequence of the foreground object, and eliminate the contour points that do not belong to the set point. 4) Based on the contour features of foreground objects, eliminate the contours of non-bait objects; 5) Calculate and sort the area of ​​each contour after the elimination process, and determine the area of ​​a single bait; 6) Estimate the bait contained in the current contour For the number of particles, the number of particles estimated by all contours is added to obtain the final number of remaining bait particles. The invention has the advantages of adapting to the water environment of turbid ponds, adapting to uneven illumination, considering adhesion, and counting accurately.

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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