In this paper, a fast defect detection method for oranges is studied from the perspective of machine vision. Firstly, in order to eliminate the interference of orange stems in defect detection, a shape-based template matching method is used to extract the orange stems region and the average pixels of the image are used to fill the region. Secondly, contrast enhancement and edge extraction are performed on the image with orange stems eliminated, and the background is removed by combining morphological processing and filling techniques to obtain the orange region. Finally, by carrying out channel separation on the orange region image, the OSTU threshold segmentation is performed on its red channel image according to the color characteristics of the orange and its defects, and the extraction and contour fitting of the defects are completed by combining area features. The experiments show that the proposed method can quickly and accurately achieve the defect detection of oranges, and then further provide the core detection technology for some automatic orange sorting.
To better achieve image correction effect, an image correction method based on corner point detection is proposed. In image preprocessing, firstly, image equalization is achieved based on the contrast limited adaptive histgram equalization to avoid the problems caused by illumination and suppress noise while maintaining details, and then adaptive threshold segmentation is performed using the OTSU to obtain the binarized image. In the corner point detection stage, the contours of the binarized image are extracted firstly and the closed contours are filled to avoid the independent contours from affecting the accuracy of region growth, then the center point of the image is used as the seed pixel for region growth, and finally the four corner points are calculated based on the linear contours of the growth region and Hough theory, where the accurate region growth result can avoid the influence of the background on the corner point detection. In the correction stage, the perspective matrix is calculated by the four corner points, and the image is corrected by the perspective transformation. The experiments show that the proposed method can accurately find the corner points of document images and achieve efficient correction.
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