Estimation of percentage of impurities in coffee using a computer vision system

ABSTRACT The quality and price of coffee drinks can be affected by contamination with impurities during roasting and grinding. Methods that enable quality control of marketed products are important to meet the standards required by consumers and the industry. The purpose of this study was to estimate the percentage of impurities contained in coffee using textural and colorimetric descriptors obtained from digital images. Arabica coffee beans (Coffea arabica L.) at 100% purity were subjected to roasting and grinding processes, and the initially pure ground coffee was gradually contaminated with impurities. Digital images were collected from coffee samples with 0, 10, 30, 50, and 70% impurities. From the images, textural descriptors of the histograms (mean, standard deviation, entropy, uniformity, and third moment) and colorimetric descriptors (RGB color space and HSI color space) were obtained. The principal component regression (PCR) method was applied to the data group of textural and colorimetric descriptors for the development of linear models to estimate coffee impurities. The selected models for the textural descriptors data group and the colorimetric descriptors data group were composed of two and three principal components, respectively. The model from the colorimetric descriptors showed a greater capacity to estimate the percentage of impurities in coffee when compared to the model from the textural descriptors.

Studying the Influence of Illumination Design in Quality Inspection on Vaccine Vials Production Line Using Computer Vision

In the industrial context, there are key factors that directly affect the system’s efficiency. Higher demands for both quantity and quality in today’s market call for constant research and development of technologies for automating production and quality control. Machine vision is a solution to increase speed and accuracy in defect detection. However, applications from machine vision are only effective if there is good data input. This is the reason why a machine vision system, needs high-quality input images from a well-designed illumination system. These illumination systems are designed to highlight faults in products. Therefore, the images obtained will provide optimized data for easier image processing thus directly increase the processing speed, accuracy, and overall system performance. To achieve this goal, this paper presents a few approaches to enhance and optimize images by implements illumination techniques into a miniature model of pharmaceutical bottle assembly line using machine vision as the inspector block. In this paper, we will evaluate the critical needs of using customize illumination system for quality inspection on an assembly line.

SYSTEM FOR PROVIDING AUTOMATIC NAVIGATION OF AN UNMANNED AERIAL VEHICLE IN THE VICINITY OF AN AIRFIELD

Рассмотрены вопросы разработки системы, способной обеспечивать автоматическую навигацию беспилотного летательного аппарата в окрестности аэродрома без использования дополнительных датчиков. Рассмотрен алгоритм решения этой задачи с использованием бортовой монокулярной системы технического зрения, функционирующей в диапазоне 1,55 мкм. Для обеспечения навигации беспилотный летательный аппарат оснащен системой информационного обмена, а в районе точки взлета-посадки в качестве наземных источников (маяков) предложено использовать полупроводниковые лазеры с некогерентным излучением длиной волны 1,55 мкм, которые обеспечивают работу системы в простых метеоусловиях. Путем измерений угла азимута в двух точках траектории движения беспилотного летательного аппарата вычисляются его координаты местоположения относительно взлетно-посадочной полосы, а также угол курса необходимый для выхода в начальную точку глиссады снижения. Ввиду того, что погрешности измерений обусловлены ошибками измерений угла азимута, курса и скорости полета, ошибками измерения временных интервалов в данной работе пренебрегаем. Полученные графики показывают, что погрешности измерения координат беспилотного летательного аппарата минимальны при пролете напротив маяка и резко возрастают при удалении от него, что обусловлено погрешностью измерения азимута и дальности. При этом измерение местоположения беспилотного летательного аппарата необходимо выполнять на минимальном удалении от маяка The article discusses the development of a system capable of providing automatic navigation of an unmanned aerial vehicle in the vicinity of an airfield without the use of additional sensors. We considered an algorithm for solving this problem using an onboard monocular vision system operating in the range of 1.55 microns. To ensure navigation, the unmanned aerial vehicle is equipped with an information exchange system, and in the area of the take-off and landing point, we propose to use semiconductor lasers with incoherent radiation with a wavelength of 1.55 microns, which ensure the operation of the system in simple weather conditions, as ground sources (beacons). By measuring the azimuth angle at two points of the trajectory of the unmanned aerial vehicle, we calculated its location coordinates relative to the runway, as well as the course angle necessary to reach the starting point of the descent glide path. Since measurement errors are caused by errors in measuring the azimuth angle, course and flight speed, we neglected errors in measuring time intervals in this work. The obtained graphs show that the errors in measuring the coordinates of an unmanned aerial vehicle are minimal when flying in front of the lighthouse and increase sharply when moving away from it, which is due to the error in measuring azimuth and range. At the same time, the measurement of the location of the unmanned aerial vehicle must be carried out at a minimum distance from the lighthouse

Human Position Detection Based on Depth Camera Image Information in Mechanical Safety

The devices used for human position detection in mechanical safety mainly include safety light curtain, safety laser scanner, safety pad, and vision system. However, these devices may be bypassed when used, and human or equipment cannot be distinguished. To solve this problem, a depth camera is proposed as a human position detection device in mechanical safety. The process of human position detection based on depth camera image information is given; it mainly includes image information acquisition, human presence detection, and distance measurement. Meanwhile, a human position detection method based on Intel RealSense depth camera and MobileNet-SSD algorithm is proposed and applied to robot safety protection. The result shows that the image information collected by the depth camera can detect the human position in real time, which can replace the existing mechanical safety human position detection device. At the same time, the depth camera can detect only human but not mobile devices and realize the separation and early warning of people and mobile devices.

Quantitative Optical Coherence Tomography for Longitudinal Monitoring of Postnatal Retinal Development

A better study of the postnatal retinal development is not only essential for the in-depth understanding of the nature of the vision system but also may provide insights for treatment developments of eye conditions, such as retinopathy of premature (ROP). To date, quantitative analysis of postnatal retinal development is primarily limited to endpoint histological examination. This study is to validate in vivo optical coherence tomography (OCT) for longitudinal monitoring of postnatal retinal development in developing mouse eyes. Three-dimensional (3D) frame registration and super averaging were adopted to investigate the fine structure of the retina. Interestingly, a hyporeflective layer (HRL) between the nerve fiber layer (NFL) and inner plexiform layer (IPL) was observed in developing eyes and gradually disappeared with aging. To interpret the observed retinal layer kinetics, a model based on eyeball expansion, cell apoptosis, and retinal structural modification was proposed.

Pose Determination of the Disc Cutter Holder of Shield Machine Based on Monocular Vision

The visual measurement system plays a vital role in the disc cutter changing robot of the shield machine, and its accuracy directly determines the success rate of the disc cutter grasping. However, the actual industrial environment with strong noise brings a great challenge to the pose measurement methods. The existing methods are difficult to meet the required accuracy of pose measurement based on machine vision under the disc cutter changing conditions. To solve this problem, we propose a monocular visual pose measurement method consisting of the high precision optimal solution to the PnP problem (OPnP) method and the highly robust distance matching (DM) method. First, the OPnP method is used to calculate the rough pose of the shield machine’s cutter holder, and then the DM method is used to measure its pose accurately. Simulation results show that the proposed monocular measurement method has better accuracy and robustness than the several mainstream PnP methods. The experimental results also show that the maximum error of the proposed method is 0.28° in the direction of rotation and 0.32 mm in the direction of translation, which can meet the measurement accuracy requirement of the vision system of the disc cutter changing robot in practical engineering application.