A machine vision system for high speed sorting of small spots on grains
2012
Pearson, Tom | Moore, Dan | Pearson, Jim
A sorting system was developed to detect and remove individual grain kernels with small localized blemishes or defects. The system uses a color VGA sensor to capture images of the kernels at high speed as the grain drops off an inclined chute. The image data are directly input into a field-programmable gate array that performs image processing and classification in real time. Spot detection is accomplished by a combination of color information and a simple, nonlinear spatial filter that detects small dips in pixel intensity along an image line. Color information is combined with spatial filtering to achieve a high level of accuracy. Testing was performed on popcorn with blue-eye damage, which is characterized by a small blue blemish on the germ. A two-camera system was developed to inspect the opposite sides of each kernel as they slide off the end of a chute. The chute was designed such that the kernels slide down the chute without tumbling, increasing the probability that a spot will be in the field of view of one of the cameras. The system’s accuracy is 89 % identification of blue-eye damaged kernels with a 6 % false positive rate. The throughput is approximately 180 kernels per second, or 100 kg/h.
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