葵花籽壳内籽仁的品质直接影响产出食用油的质量, 利用太赫兹时域透射成像技术, 结合形态学滤波和K-均值图像分割的方法, 对葵花籽破损粒、 虫蚀粒、 空壳粒三种异常样本进行带壳无损检测, 实现对葵花籽壳内品质的探索研究。 参考国家标准和前人经验制备破损粒、 虫蚀粒和空壳粒三种带壳葵花籽样本, 利用太赫兹时域光谱仪TeraPulse 4000及透射成像附件, 以0.2 mm的分辨率分别采集上述三种异常样本的光谱图像, 并采集一颗正常葵花籽的光谱图像作为参照, 以峰峰值成像的方式对四种葵花籽太赫兹图像进行重构。 通过太赫兹图像可初步判断壳内籽仁的形态, 但仍存在对比度低、 边缘信息模糊等问题, 需要进一步优化处理。 采用形态学滤波算法对葵花籽的太赫兹图像进行滤波, 选择边长为3的平坦型菱形结构元素作为核对图像进行一次膨胀, 再计算图像的外部梯度, 完成对图像的滤波处理; 将形态学滤波结果分别与中值滤波、 均值滤波和非局部均值滤波结果进行比较, 对比发现, 形态学滤波不仅保证了图像的清晰度, 保留了边缘信息, 同时能使葵花籽样本与背景之间存在明显界限, 有利于后续进行图像分割处理。 为了更加准确的检测葵花籽籽仁的形态, 对滤波后图像进行图像分割。 采用K-均值聚类算法对滤波后的太赫兹图像进行图像分割; 为提高分割结果的准确性, 对不同样本的图像分别确定了不同的初始聚类中心个数 K, 其中破损粒 K=4、 虫蚀粒 K=5、 空壳粒 K=3、 正常粒 K=4, 分割后的图像能准确呈现葵花籽壳内籽仁的形态。 研究结果表明, 太赫兹时域透射成像技术结合形态学滤波及K-均值图像分割方法对实现葵花籽内部品质的带壳无损检测具有可行性, 为后期建立带壳葵花籽品质无损检测模型奠定基础, 为油料作物内部品质的带壳无损检测提供参考。

The quality of the kernels in the sunflower seed shell directly affects the quality of the edible oil. Using terahertz time-domain transmission imaging technology combined with morphological filtering and K-means image segmentation, the three abnormalities of sunflower seed like kernel damaged, kernel worm-eaten and empty seed were investigated to achieve to explore the quality of seed kernels in sunflower seed hull. According to the national standards and previous experience, three kinds of shelled sunflower seeds samples of kernel damaged, kernel worm-eaten and empty shells were prepared. The terahertz time domain spectrometer TeraPulse 4000 and transmission imaging accessory were used to acquire the spectral images of the above three abnormal samples at a resolution of 0.2 mm, and a spectral image of normal sunflower seed was taken as a reference. Four sunflower seed terahertz images were reconstructed by peak-to-peak imaging. The terahertz images can preliminarily determine the shape of the seed kernel in the shell, but there were still problems such as low contrast and blurred edge information, which needed further optimization. The morphological filtering algorithm was used to filter the terahertz images of sunflower seeds. The flat diamond structure element with a side length of 3 was selected as the collation image for one expansion, and then the external gradient of the image was calculated to complete the filtering process of the image. At the same time, the morphological filtering results were compared with the median filtering results, the mean filtering results and the non-local mean filtering results. It was found that the morphological filtering not only ensured the sharpness of the image, but also preserved the edge information, and could also make an obvious boundary between the sunflower seed sample and background, which was conducive to subsequent image segmentation processing. In order to more accurately detect the shape of the sunflower seed kernel, the filtered images were segmented. The K-means clustering algorithm was used to segment the filtered terahertz images. In order to improve the accuracy of the segmentation results, the number K of different initial cluster centers was determined for the images of different samples, among which the kernel damaged K=4, the worm-eaten kernel K=5, the empty shell K=3, the normal grain K=4. The segmented images accurately showed the morphology of the kernels in the sunflower seed hull. The study showed that the terahertz time-domain transmission imaging technique combined with morphological filtering and K-means image segmentation method was feasible for the non-destructive detection of the internal quality of sunflower seeds, which laid a foundation for the establishment of non-destructive testing model for the quality of shelled sunflower seeds, and provided a new method reference for nondestructive detection of the internal quality of shelled oil crops.