基于小波变换与Otsu阈值去噪的脐橙识别方法

【目的】解决在农业环境中识别脐橙的目标区域存在的噪声干扰、检测效果不理想等问题。【方法】提出一种基于小波变换与Otsu阈值去噪的脐橙识别方法。首先选择较好的对比度,建立有利于图像分割的YCbCr颜色模型;然后设计一种基于Otsu阈值去噪的脐橙检测算法,进而减少脐橙分割区域的噪声干扰;最后提出质心补圆法确定脐橙在图像中的位置,并在原始图像中显示检测结果。【结果】泛青色和橙色脐橙识别率分别为87.10%和94.18%,顺光和逆光情况下脐橙识别率分别为92.96%和90.15%,遮挡和未遮挡情况下脐橙识别率分别为90.82%和93.18%,总识别率为92.07%。【结论】该方法环境适应性强,适用于农业环境下不同遮挡、光照和表皮颜色情况的脐橙图像识别处理。

Navel orange recognition based on wavelet transform and Otsu threshold denoising

Objective To solve the existing problems of noise interference and ineffective detection in the target area during navel orange recognition in agricultural environment.Method A navel orange recognition method based on wavelet transform and Otsu threshold denoising was proposed. Firstly, we chose a better contrast and established the YCbCr model with color space helpful for image segmentation. Then we designed a navel orange detection algorithm based on Otsu threshold denoising and reduced noise interference in the segmentation region of navel orange. Finally we proposed a circle filling method based on the center of mass to determine the position of navel orange in the image, and the detection result was displayed in the original image.Result The recognition rates of cyan and orange navel oranges were 87.10% and 94.18%, respectively. The recognition rates of navel orange were 92.96% and 90.15% respectively under direct light and backlight, and 90.82% and 93.18% respectively under occluded and unoccluded situation. The total recognition rate was 92.07%.Conclusion The method has strong environmental adaptability, and is suitable for the identification and processing of navel orange images under different occlusion, light and skin color conditions in agricultural environment.