基于计算机视觉的牛肉分级技术研究进展

自从主要的牛肉生产国相继颁布牛肉分级系统以来,计算机视觉牛肉分级技术一直就是牛肉分级领域中的研究重点。本文概述了目前世界上主要的牛肉分级体系,着重论述了国内外计算机视觉牛肉分级技术的发展情况,提出了现行的计算机视觉牛肉分级技术面临的主要问题及其发展方向。      

基于计算机视觉的牛肉质量分级研究进展

牛肉品质分级对于肉牛产业具有重要意义,为克服人工评级的弊端,客观、无损的自动分级技术成为研究热点。本文综述了国内外利用计算机视觉对牛肉大理石纹、生理成熟度、肉色和脂肪色这些指标进行分级预测的研究进展情况,讨论了研究过程中诸如图像分割、特征提取方面存在的困难,最后指出了计算机视觉技术在牛肉品质分级应用过程中存在的问题以及可能的发展方向。     

计算机视觉在牛肉自动分级技术中的应用

国外计算机视觉牛肉自动分级技术较为成熟,国内的研究也取得了一定的成果。鉴于国内牛肉分级意义重大,本文概述了我国牛肉质量等级标准和国内外研究现状,并针对利用计算机视觉技术对牛肉自动分级,从完善标准、调整工艺、设计成像条件和计算方法等多个方面提出所遇到的问题,并指出牛肉自动分级技术的研究方向。     

计算机视觉技术在食品分级中的应用

计算机视觉技术在发达国家已经得到广泛的应用,在农产品和食品工业中的应用也不断开展。本文主要从颜色检测、形状检测和缺陷检测三个方面论述计算机视觉技术在食品分级中的重要作用。     

基于计算机视觉的槟榔分级研究

槟榔品种的分级技术目前比较落后,主要靠人工完成分级,因而品种分级的质量难以得到保证。该试验用计算机视觉技术进行品种分级,通过图像获取、预处理等提取其颜色特征、形状特征和纹理特征,通过试验分析,发现由颜色和形状特征参数结合起来即可以获得较好的效果。并对其进行主成分分析后代入支持向量机进行分级,预测集的正确识别率达到90.38%以上。     

我国基于机器视觉的水果自动分级技术及研究进展

综述了国内基于机器视觉的水果自动分级技术在大小、形状、颜色和表缺陷分级方面的研究现状,分析了基于机器视觉的水果自动分级过程中存在的不足,提出了利用定向装置与机器视觉相结合的水果分级的思路。     

基于计算机视觉的柑橘糖度检测

现场采摘180个涟红温州蜜柑,考虑色泽和大小范围的广度,从中选取140个作为试验样本,各果转90.中心角,采集一幅图像,每果采图4幅,通过图像裁切,RGB空间至HSI空间的转换和图像差值法去背景,提取柑橘色调H和饱和度S表面色泽参数,用110个样本训练小波神经网络,30个样本检验网络性能.试验结果表明,检测最大绝对偏差0.2452°Brix,最小绝对偏差0.0002°Brix,平均偏差0.0545°Brix,标准差0.0830°Brix,精度在±0.1°Brix内的正确识别率为73.33%,精度在±0.2 °Brix内的正确识别率为90%.     

基于计算机视觉技术的稻米检测研究

综述了计算机视觉技术在稻米品种、外形轮廓、垩白度、加工精度、留胚率、整精米率、黄粒米和稻米化学成分等检测中的应用。     

基于计算机视觉的织物疵点检测技术研究进展

对基于计算机视觉的织物疵点检测技术进行回顾,介绍了灰度共生矩阵法,局部二值模式算法,邻域关联分析,自组织映射,支持向量机,学习向量量化分类器,多分类器组合和决策融合等算法等在图像预处理,特征提取、分类和识别等方面的应用情况,着重讨论了一种基于多数投票原则的多分类器决策融合技术,试验结果证实该技术有较高精确性.     

我国屠宰企业猪胴体分级技术的研究进展与建议

文中叙述了研究猪肉品质的重要性,介绍了国内外的猪胴体分级标准,综述了多种检测猪肉品质的先进技术和方法,描述了利用计算机视觉技术获取多指标、多特征研究猪肉品质是我国屠宰企业猪胴体分级的发展趋势。     

An expert egg grading system based on machine vision and artificial intelligence techniques

The main purpose of this research was design and development of an intelligent system based on combined fuzzy logic and machine vision techniques for grading of egg using parameters such as defects and size of eggs. The detected defects were internal blood spots, cracks and breakages of eggshell. The Hue-Saturation-Value (HSV) color space was found useful in obtaining visual features during Image Processing (IP) stage. The fuzzy inference system (FIS) was designed based on triangular and trapezoidal membership functions, fuzzy rules with logical operator of AND inference system of Mamdani and method of center average for defuzzifier. The evaluation results of IP algorithms showed that use of IP technique has good performance for defects and size detection. The Correct Classification rate (CCR) was 95% for size detection, 94.5% for crack detection and 98% for breakage detection. The overall accuracy FIS model in grading of the eggs was 95.4.     

基于机器视觉的牡蛎分级设备设计

目的：提高牡蛎分级的精确性和全面性。方法：提出并设计了牡蛎自动化分级设备,确定了旋转滚筒与挡板传送带结合的牡蛎排队结构、质量检测和机器视觉检测相结合的分级方式,完成了牡蛎分级设备的整体结构设计。通过工业相机采集牡蛎图像,使用大津法二值化、高斯滤波处理、Canny算子边缘提取等方法提取牡蛎图像,通过机器视觉算法以长度和饱满度为标准对牡蛎进行分级,并进行机器视觉分级与人工分级对比试验。结果：该设备分级准确率为95.4％,图像检测速度约为0.647 s/幅。结论：机器视觉对牡蛎分级是有效的,可以较为准确地对牡蛎进行分级。     

基于机器视觉的水果分级系统

目的:解决目前水果分级检测方法效率低、误检率高等问题。方法:以苹果为分拣对象,设计一个基于机器视觉的水果分级系统。对实时采集得到的苹果图像进行预处理,使用改进的Canny边缘检测算法进行边缘提取,通过最小外接圆法拟合边缘坐标得到苹果的横切面半径。将采集到的RGB图像转换为HSI图像,根据H分量范围计算红色区域比例,判断苹果的色泽度。统计区域像素点个数,分别求取苹果的面积和周长,计算出苹果的圆形度。结合苹果果径长度、色泽度和圆形度3个特征值对苹果进行综合分级。结果:50个苹果样本试验结果表明,水果分级系统和人工分拣测量的果径误差范围在±1.5 mm以内,样本颜色特征与苹果实际外观相符,圆度值的大小与实际形状优劣相符。结论:该系统满足实际生产中对于苹果分级的需求,有助于实现苹果品级的准确识别。     

Monitoring and grading of tea by computer vision - A review

Tea being a high value crop throughout the world, its quality plays a significant role in its marketability. Currently, organoleptic methods such as inspection by human sensory panel, and instrument based approaches such as gas chromatography and colorimetric method have been reported as the quality monitoring tools in various stages of tea processing. These methods are time consuming, laborious, expensive and sometimes inaccurate. Therefore, to overcome the inaccuracy and inconsistency, computer vision techniques can be explored as an alternative to conventional techniques. This paper presents an overview of various computer vision based algorithms for colour and texture analysis with a special orientation towards monitoring and grading of made tea. Computer vision and image analysis are non-destructive procedures for sorting tea on the basis of its physical parameters viz. granule colour, shape, size and texture. Although diverse methods for estimation of above parameters were developed by researchers independently, all these can be related to each other.     

基于计算机视觉的午餐肉颜色测定系统研究

目的建立一种对午餐肉样品物理特性要求较少,能对物料表面整体颜色进行准确测量的无损检测方法。方法采用计算机视觉系统对24色色彩测试板测定得L,a,b值,使用色彩色差计对24色色彩测试板测得L~*,a~*,b~*值,对两组数据进行线性回归;计算机视觉系统测定午餐肉的L,a,b值,带入回归方程得到标准的L,a,b值,色彩色差计对午餐肉测定得L~*,a~*,b~*值,用SPSS软件对得到的标准L,a,b值和L~*,a~*,b~*值进行成对样本检验。结果 L,a,b值回归方程的相关系数r~2分别为0.9900、0.9707和0.9801,有高度相关性;午餐肉标准L,a,b值和L~*,a~*,b~*值成对样本检验得到的P值分别为0.146、0.087、0.109,大于显著性水平0.05,回归方程转换值与色差计测定结果无显著差异。结论本文建立的基于计算机视觉的午餐肉颜色测定方法可以准确测定午餐肉颜色,其效果可以代替色差计。     

基于深度学习的牛肉大理石纹智能分级研究

目的开发客观、准确、无损的基于深度学习的牛肉大理石纹智能化分级技术。方法将深度学习的图像识别方法应用于牛肉大理石纹的特征提取和分类上,并进行相应的调试和学习。结果通过计算机调试和学习,评级正确率分别达到84.2%(一级)、89.4%(二级)、81.9%(三级)、84.1%(四级)、82.6%(五级)。各级牛肉的识别率均在80%以上,识别时间都在1 s以内,达到了预期目标。结论将深度学习的图像识别方法应用于牛肉大理石纹的特征提取和分类上,评级准确率非常高,且随着图片数据库样本数的不断增多,其识别的准确度将不断提高,可进行大量推广使用。     

Quality grading of Atlantic salmon (Salmo salar) by computer vision

ABSTRACT:  In this study, we present a promising method of computer vision-based quality grading of whole Atlantic salmon ( Salmo salar ). Using computer vision, it was possible to differentiate among different quality grades of Atlantic salmon based on the external geometrical information contained in the fish images. Initially, before the image acquisition, the fish were subjectively graded and labeled into grading classes by a qualified human inspector in the processing plant. Prior to classification, the salmon images were segmented into binary images, and then feature extraction was performed on the geometrical parameters of the fish from the grading classes. The classification algorithm was a threshold-based classifier, which was designed using linear discriminant analysis. The performance of the classifier was tested by using the leave-one-out cross-validation method, and the classification results showed a good agreement between the classification done by human inspectors and by the computer vision. The computer vision-based method classified correctly 90% of the salmon from the data set as compared with the classification by human inspector. Overall, it was shown that computer vision can be used as a powerful tool to grade Atlantic salmon into quality grades in a fast and nondestructive manner by a relatively simple classifier algorithm. The low cost of implementation of today's advanced computer vision solutions makes this method feasible for industrial purposes in fish plants as it can replace manual labor, on which grading tasks still rely.     

A novel approach to grading pork carcasses: computer vision and ultrasound

A Computer Vision System prototype for grading pork carcasses was developed at the Lacombe Research System. The system consists of two components: ultrasound imaging to scan a cross-section of the loin muscle and video imaging to capture two-dimensional (2D) and three-dimensional (3D) images of the carcass. For each of the 241 carcasses (114 barrows and 127 gilts), salable meat yield was determined from a full cutout. Linear, two- and three-dimensional, angular and curvature measurements and carcass volume were derived from each image. Muscle area and fat thickness (7 cm off the mid-line) measured by ultrasound at the next to last rib site, together with 2D and 3D measurements provided the most accurate model for estimating salable meat yield (R²=0.82 and RSD=1.68). Models incorporating fat thickness and muscle depth measured at the Canadian grading site (3/4 last rib, 7 cm off the mid-line) with the Destron PG-100 probe, had the lowest R² and highest residual standard deviation (RSD) values (R²=0.66 and RSD=2.15). Cross-validation demonstrated the reliability and stability of the models; hence conferring them good industry applicability. The Lacombe Computer Vision System prototype appears to offer a marked improvement over probes currently used by the Canadian pork industry.