Grain and chaff separation detection method based on machine vision

Abstract

[Objective] To solve the problem of poor manual detection accuracy of traditional grain and chaff separator and improve production efficiency. [Methods] An image detection method based on machine vision was proposed, which realized the feature recognition and separation of grain rough through multi-stage progressive fusion of different image algorithms. The acquired images were selected in the ROI region and enhanced by Retinex algorithm. The Otsu algorithm was used to segment the image, and then the median filtering wwas combined with morphology to remove the image noise. The improved Canny algorithm was used to detect edge features of binary images, and the position information of the contour of the valley rough image was extracted by combining the Hough transform. Finally, the state estimation of the position information was performed by using the Kalman filter, and the best predicted value of the separated position was obtained, while the position offset error was reduced. [Results] The average detection error of the system was 3.14 mm, a decrease of 1.82 mm compared to before filtering, and the average standard deviation of filtering error was 0.8 mm. [Conclusion] This method can effectively detect the grain rough feature information and improve the separation accuracy.

Keywords

• grain and chaff separation
• machine vision
• image processing
• feature extraction