玉米深床干燥过程中杂质含量的在线检测是保证稳定运行的关键技术,为干燥设备的自适应控制提供数据支持。在这项研究中,一种自动的玉米图像采集方法,杂质分类和识别,提出了基于机器视觉技术的杂质含量检测。利用多尺度带色彩恢复的retinex(MSRCR)算法对原始图像进行增强,以消除噪声的影响。HSV(色调,饱和度,值)为图像分割设置颜色空间参数阈值,并结合形态学运算得到分类识别结果。采用综合评价指标对试验结果进行定量评价。在线检测结果表明,玉米芯破碎的综合评价指标,破碎的苞片,碎石占83.05%,83.87%,和87.43%,分别。该算法能够快速有效地识别玉米图像中的杂质,为玉米深床干燥过程中杂质含量的监测提供技术支持和理论依据。
Online detection of impurities content in the corn deep-bed drying process is the key technology to ensure stable operation and to provide data support for self-adapting control of drying equipment. In this study, an automatic approach to corn image acquisition, impurity classification and recognition, and impurities content detection based on machine vision technology are proposed. The multi-scale retinex with colour restore (MSRCR) algorithm is utilized to enhance the original image for eliminating the influence of noise. HSV (Hue, saturation, value) colour space parameter threshold is set for image segmentation, and the classification and recognition results are obtained combined with the morphological operation. The comprehensive evaluation index is adopted to quantitatively evaluate the test results. Online detection results show that the comprehensive evaluation index of broken corncobs, broken bracts, and crushed stones are 83.05%, 83.87%, and 87.43%, respectively. The proposed algorithm can quickly and effectively identify the impurities in corn images, providing technical support and a theoretical basis for monitoring impurities content in the corn deep-bed drying process.