酿酒高粱不完善粒检测仪检测模型的研究与检验

针对现阶段酿酒企业检测高粱不完善粒效率较低和识别率不高等问题,结合市场上现有的粮食不完善粒检测仪器,开发了一套基于图像识别的高粱不完善粒快速检测仪,对图像的采集、关键硬件、机器视觉和深度学习等方面做了一系列研究,研究分别采用单一特征分析技术、基于机器学习的图像分类技术、基于深度学习的图像分类技术、细粒度图像分类技术对高粱图片进行分类识别分析,通过对比,最终利用Tensorrt部署技术将细粒度图像分类网络部署到设备中。结果表明,开发的高粱不完善粒快速检测仪的识别精度与人工检测的平均误差控制在1%以内;50 g高粱样品的检测时间控制在5min以内。相较于传统的人工检测,检测时间大大缩短,同时避免了人工检测主观上的偏差,对于酿酒企业的高粱不完善率检测鉴定具有重要意义。

Study and test of the model of unsound kernel measuring instrumengt for distiller sorghum(Online First, Recommended Article)

In view of the low efficiency and low recognition accuracy of sorghum unsound kerneld etection in wine-making enterprises at the present stage, the sorghum unsound kernel detection instrument with image recognition, which was combined with the existing cereal unsound kernel detection instruments in the market, was developed. A series of research was focus on the image collection, key hardware, machine vision and deep learning. In this study, single feature analysis technology, machine-learning based image classification technology, deep-learning based image classification technology and fine-grained image classification technology were used to classify and identify sorghum images, respectively. By contrast, tensorrt deployment technology was used to deploy fine-grained image classification network into the device. The results showed that the recognition accuracy of the developed rapid sorghum imperfect grain detector was less than 1% of the average error of manual detection. The detection time of 50 g sorghum sample was controlled within 5 minutes. Compared with the traditional manual detection, the detection time was greatly shortened, and the subjective deviation of manual detection was also avoided. It is of great significance for the detection and identification of sorghum imperfection rate in wine-making enterprises.