摘要
设计一种基于机器视觉的鲜食玉米品质检测分类器。利用计算机视觉技术,通过小波分析方法对不同角度拍摄的鲜食玉米图像进行纹理特征分析;在获取玉米图像纹理特征的基础上,采用最大熵函数对纹理图像的分离度进行度量,并结合重量判据设计鲜食玉米品质检测分类器,实现对不同品种、尺寸以及破损程度的鲜食玉米进行分类,有效剔除病虫害污染的玉米产品。该设备可有效减少因工人主观经验水平的参次不齐等主观因素导致产品质量检测分类不均的现象。经实验验证,该品质检测分类器能够有效完成不同重量、尺寸的鲜食玉米的产品品质检测与分类,有效分类率可达到99%以上。
In the deep processing of fresh corn, the detection and classification of fresh corn quality are an important but tedious process. The traditional treatment needs a lot of experienced workers to complete this operation, while the results of product quality detection and classification are affected by the subjective experience factors. In order to ensure product quality and increase productivity, an automatic detection and classification algorithm for corn product quality and the equipment are designed in this paper. This automatic device consists of the vision acquisition module, detection and classification control module and execution control module. The vision acquisition module acquires the images of products through the cameras which are installed on the device. In the equipment design process, 2 work stations are designed to capture the images of fresh corn in different view, and the position of fresh corn product is rolled over by a designed mechanical device which is driven by a step motor. In order to provide high quality images for the detection and classification, a light emitting diode（LED） light source is installed near the camera and lighting the measured product during the process of image acquisition; the detection and classification control module is the control core, and it accomplishes the image filtering, texture feature extraction and product classification. In the end of detection and classification operation, this module will export the control instruction to the execution control module. The execution module consists of motion control card, servo controller and servo motor. Using these components, this module moves for special degrees according to the control instruction, and sends the measured products to the designated storage location. In the detection and classification algorithm design process, the computer vision technology is used to detect the fresh corn images and extract texture feature of image. At first, we capture the fresh corn images from different angles of view, and th
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2016年第1期298-303,共6页
Transactions of the Chinese Society of Agricultural Engineering
基金
河北省自然科学基金(F2015203287)
关键词
计算机视觉
作物
分类器
纹理特征
小波分析
视觉熵
computer vision
crops
classifier
texture feature
wavelet analysis
visual entropy
