Abstract: The detection of impurities in rapeseed combine harvester mainly depends on labor, which has low efficiency and poor realtime performance, resulting in the lack of basis for the regulation of harvester operation parameters and unstable harvest quality. In order to solve the above problems, this paper proposed a visual recognition algorithm for rapeseed impurities, and developed an online detection system for impurities. Based on the HSV color space model, the brightness distribution law of rapeseed image under single side strip light source, double side strip light source and central ring light source in the guided impurity content detection device was explored. The results showed that the brightness variation coefficient of the image under the central ring light source was the smallest and the brightness uniformity of the image was the best. The distribution intervals of color characteristic parameters were compared in the loworder moment of the HSV color space model. The results showed that the characteristic parameters range of rapeseed grains and impurities in the H component were the most significant. Combined with the spherical characteristics of rapeseeds and impurities, a segmentation algorithm considering color and morphological characteristics was proposed. Through the calibration test, the relationship model between the quality of rape grain and impurity and its pixel number was constructed. The number of pixels was converted into the actual quality to realize the online detection of rapeseed impurity content. The bench test showed that the precision rate of rape impurities was 91.6%, the recall rate was 89.5%, and the average error of impurity content detection was 14.8%. It can accurately identify the impurities in rapeseeds and calculate the impurity content in real time.

Key words: rapeseed, impurity detection, combine harvester, sampling device, shape feature, HSV

摘要： 针对目前油菜联合收获机含杂率检测主要依靠人工、效率低、实时性差、收获机作业参数调控缺乏依据、收获质量波动大等问题，设计导流式含杂率检测装置，提出油菜杂质视觉识别算法，开发含杂率在线检测系统。基于HSV颜色空间模型，探究导流式含杂率检测装置中单侧条形光源、双侧条形光源和中心环形光源下油菜图像的亮度分布规律，结果表明中心环形光源下图像各像素点的亮度变异系数最小，图像亮度均匀性最好。对比分析含杂油菜图像在HSV颜色空间模型中前三阶像素矩阵各颜色特征参数的分布区间，结果表明油菜籽粒、杂质在H分量中的特征参数范围差异性最显著，并结合油菜籽粒、杂质的圆形度特征，提出综合考虑颜色、形态特征的油菜杂质分割算法。通过标定试验建立油菜籽粒、杂质质量与其像素数的拟合模型，将油菜籽粒和杂质的像素数转换为实际质量，实现油菜含杂率在线检测。台架试验表明，油菜杂质的查准率为91.6%，查全率为89.5%，含杂率检测平均误差为14.8%，能够准确识别油菜籽粒中的杂质并实时计算含杂率。

关键词: 油菜, 含杂检测, 联合收获机, 采样装置, 形状特征, HSV