Recognizing and Positioning of the Pig Manure in a Simulated Pig Pen by Computer Vision
Date Issued
2008
Date
2008
Author(s)
Chen, Li-En
Abstract
In order to achieve the automatic manure removal in pig pens, a computer vision system was developed to accurately position the simulated manures thru the camera image of the simulated pig pen in this study. The simulated pig pen including simulated pigs and simulated manures was constructed. An image acquiring system, including camera, dimmer, incandescent lamp, photometric sensor and photometer was set up for testing the computer vision system. The top view image was taken by the camera mounted on top of the simulated pig pen while the ambient luminosity was adjusted by using the dimmer. In experimental design, 9 different distribution patterns and 5 levels of luminosity, totally 45 conditions were tested for evaluating the positioning efficiency of the system. The parameters used in tests include the minimum enclosing rectangle (MER), density, self-defined mask and the gradient threshold value. The image was acquired at the previous 45 different conditions, and there were 10 gradient threshold levels for each condition, thereafter 450 tests were conducted in this study. Two indices of positioning efficiency, error rate and missing rate, were recorded for each test. These data were presented by drawing the three-dimension curved surfaces and their top views by using the luminosity as the x-axis, the gradient threshold value as the y-axis and the missing rate (or error rate) as the z-axis. The optimal condition was then investigated through the above three-dimension drawings. Experimental results showed that the optimal recognizing effect of 4.57% of average missing rate and 8.21% of average error rate was achieved under the condition of 500 lux of luminosity and gradient threshold value of 40. Also, except the three distribution patterns - “scattered pigs, clustered manures”, “randomized pigs, clustered manures” and “clustered pigs, clustered manures”, the missing rate and error rate of the system were all less than 10%.
Subjects
simulated pig pen
manure collection
computer vision
image processing
Type
thesis
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