Application Of Machine Vision Technology Research Articles

A grader for fresh jujube with electromagnetic synchronous actuator assembly

AbstractIn recent years, the post‐harvest commercialization of fresh jujube sorting technology is backward, the efficiency of manual grading is low, and the application of machine vision technology in fruit grading has been a research hotspot. To improve the grading accuracy and speed of the fresh jujube machine vision grading machine, this study designs a method of fresh jujube machine vision grading using an electromagnetic synchronous actuator. The grading machine consists of an automatic distribution and arrangement system, an image acquisition and processing system, and a synchronous actuator assembly, which uses a sensitive electromagnet to adsorb a silicon steel switching buckling plate to execute the action. The image processing process includes an image color component, Sobel operator filtering, Otsu method binarization, and ellipse fitting to extract the color and size eigenvalues. The experimental results show that the date images can be acquired quickly and processed accurately with a processing speed of 50 ms per frame. In addition, the proposed actuator can be sensitively triggered with a response time of 40 ms. The grader successfully classified jujube into five size classes and one color class. The grading accuracy and non‐destructive rate reached 93.7% and 100%, respectively. The grading efficiency is 20 jujubes per second, with a maximum production rate of 1400 kilograms per hour. This grading machine realizes the requirements of high accuracy, high speed, and non‐destructive grading of fresh jujube and provides an important technical and theoretical basis for the development of machine vision jujube rapid grading technology.Practical applicationsIn recent years, sorting technology in the post‐harvest commercialization of fruits has been outdated, and manual grading has been inefficient. The application of machine vision technology in fruit grading has been a research hotspot in recent years. This study designs a machine vision grading of fresh dates with an electromagnetic synchronous actuator. The grader was composed of a roller conveyor, synchronous actuator assembly, image main processor, and actuator controlling co‐processor. Experimental results indicated that jujube images could be acquired rapidly and processed accurately, with a processing speed of 50 ms per frame. In addition, the proposed actuator could be sensitively triggered with a response time of 40 ms. Finally, the grader successfully sorted jujubes into five size classes and one color class. The grading accuracy rate and non‐destructive rate reached 93.7% and 100%, respectively. The grading efficiency was 20 jujubes per second, and the maximum productivity was 1400 kg/h.

Explore the Application of Machine Vision Technology in the Automation of Machinery Manufacturing

The continuous acceleration of China’s industrialization process has made automation and intelligent technologies increasingly dominant. Implementing these technologies can enhance machining and production efficiency, free up manpower, automate machinery manufacturing processes, and yield greater economic benefits. Thus, this paper outlines the key applications of machine vision technology in modern machinery manufacturing automation. It discusses how machine vision can contribute to core functions such as workpiece measurement, detection, and robot welding, facilitating automation, intelligence, and sustainable development. The aim is for the findings of this paper to offer valuable insights into relevant professions and production processes, thereby catalyzing the pace of mechanical manufacturing automation.

Early detection of infestation by mustard aphid, vegetable thrips and two-spotted spider mite in bok choy with deep neural network (DNN) classification model using hyperspectral imaging data

Although exclusion measures (e.g., air filters, biosecurity practices) can be employed to prevent occurrence of pest outbreaks, indoors vegetable farms in Singapore are still susceptible to various arthropod pests. Due to strong interest from the industry to pursue pesticide-free production, indoors pest management is often focused on early detection for timely containment and eradication, implying the importance of robust and vigorous pest monitoring programs. In recent years, application of machine vision technologies, especially hyperspectral imaging (HSI), has been studied for their capacity to early detect pest infestation. However, there is a lack of studies conducted in actual indoor environments and on multiple arthropod pests. Thus, this study aimed to non-destructively collect hyperspectral data of bok choy Brassica rapa subspecies chinensis which were healthy or infested with either mustard aphids Lipaphis erisymi, vegetable thrips Echinothrips americanus or two-spotted spider mites Tetranichus urticae in indoor environment to build deep neural network (DNN) classification model for early detection. Based on HSI data of control and infested plants collected daily over a period of two weeks, we found that point percentage change (PPC) values associated with leaf reflectance in 420–440 nm, 500–520 nm, 620–637 nm, 720–800 nm, and 850 nm were sensitive to infestation by the mentioned arthropod pests. Deep Neural Network (DNN) classification models trained on collected HSI data were found to outperform Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) classification models. DNN models achieved 92.8 ± 0.4 % overall classification accuracy across all days. As early as two days after infestation, DNN models could achieved classification precision values of 96.4 %, 96.9 %, 93.9 % and 100 % for control plants and plants infested with either aphids, spider mites or thrips respectively. These results highlight the feasibility of multiclass early detection of different arthropod pests and the potential of HSI system coupled with DNN classification as an autonomous plant health monitoring tool in indoor crop production.

Hardware and Software Solutions for the Generation of a Database of HSV-Color Characteristics for the Main Ores and Rocks of the Khibiny Massif

This article presents developed hardware and software solutions based on the application of machine vision technology. The hardware and software solutions were created in order to generate a database of HSV-color value for the main ore types, host rocks, and minerals to define criteria for the in-process identification of the Khibiny apatite in testing the walls of blasting boreholes. The hardware ensures a multi-parametric assessment of the optical characteristics of samples and minerals located on their surface. The designed software solution allows the user to control the measurement process; systematize a description of the textural and structural features of the sample under study; and process images of the core surface. The resultant database of HSV-color value for the main ores and rocks of the Khibiny massif and their constituent minerals will provide an opportunity to search for criteria for the in-process identification of the Khibiny apatite in a mineral mixture.

Research on the application of machine vision in the design of machine automation

Abstract The application of machine vision technology to mechanical automation design is an inevitable measure to improve the comprehensive productivity of mechanical production manufacturing. In this paper, a mechanical automation system based on SOA architecture is designed to cover the image acquisition, processing, and recognition workflow of an image processing module based on machine vision. The binocular camera calibration is completed by the Zhang Zhengyou calibration method. The image is preprocessed by using algorithms such as the mean value method, segmented linear transformation and median filtering, and an algorithm is proposed to improve the ORB feature point extraction, which can extract and match the feature points quickly and efficiently. Meanwhile, a Harris corner point detection algorithm is proposed to improve the SIFT algorithm to enhance the accuracy of target recognition and localization. The designed mechanical automation system is applied to the lychee picking robot, for example, analysis, which shows that the overall recognition P-R value of the system reaches 0.953, and the classification accuracy is above 0.917. In the mechanical automation route localization, the lateral deviation is lower than 4.80cm. The maximum time for image processing and parameter transfer is 117.966ms, which indicates that the system involved in this paper is relatively stable in operation and has a better effect on the localization of the navigation line, which is of certain application value in the field of mechanical automation design.

The Application Status and Trends of Machine Vision in Tea Production

The construction of standardized tea gardens is the main trend in the development of modern agriculture worldwide. As one of the most important economic crops, tea has increasingly stringent requirements placed on its planting capacity and quality. The application of machine vision technology has led to the gradual development of tea production moving towards intelligence and informatization. In recent years, research on tea production based on machine vision technology has received widespread attention, as it can greatly improve production efficiency and reduce labor costs. This article reviews the current application status of machine vision technology in tea pest monitoring, intelligent harvesting, quality evaluation, and classification, and analyzes and discusses specific challenges around machine vision technology in tea production; for example, this technology lacks a standard database and weather interference, training errors in the model, and differences in the hardware computing speed can become a challenge. Based on the current research and application of machine vision technology in various fields, this article looks ahead to the development prospects and future trends of machine vision technology in tea production applications, such as future research to further integrate multiple types of sensors, improvements in the quality and usability of datasets, optimized model algorithms for existing problems, dissemination of research results, and intelligent management of tea production through machine vision technology.

Application of Machine Vision Technology in Citrus Production

The construction of standardized citrus orchards is the main trend in the future development of modern agriculture worldwide. As the most widely used and mature technology in the agricultural field, machine vision has greatly promoted the industrial development model of the citrus industry. This paper summarizes the application of machine vision technology including citrus pest and disease detection, harvesting identification and localization, and fruit grading. We compare the advantages and disadvantages of relevant research, and analyze the existing problems and prospects for future research. Due to the complex and changeable in-field environment, robots may experience unpredictable interference in the recognition process, which leads to errors in target fruit localization. The lack of datasets also affects the accuracy and stability of the algorithm. While expanding the dataset, it is necessary to conduct further research on the algorithm. In addition, the existing research focuses on indoor monitoring methods, which are not practical for the changeable outdoors environment. Therefore, realizing the diversity of sample datasets, designing agricultural robots suitable for complex environments, developing high-quality image processing hardware and intelligent parallel algorithms, and increasing dynamic monitoring methods are the future research directions. Although machine vision has certain limitations, it is still a technology with strong potential for development.

Research on Defect Detection Method of Nonwoven Fabric Mask Based on Machine Vision

During the production, transportation and storage of nonwoven fabric mask, there are many damages caused by human or nonhuman factors. Therefore, checking the defects of nonwoven fabric mask in a timely manner to ensure the reliability and integrity, which plays a positive role in the safe use of nonwoven fabric mask. At present, the wide application of machine vision technology provides a technical mean for the defect detection of nonwoven fabric mask. On the basis of the pre-treatment of the defect images, it can effectively simulate the contour fluctuation grading and gray value change of the defect images, which is helpful to realize the segmentation, classification and recognition of nonwoven fabric mask defect features. First, in order to accurately obtain the image information of the nonwoven fabric mask, the binocular vision calibration method of the defect detection system is discussed. On this basis, the defect detection mechanism of the nonwoven fabric mask is analyzed, and the model of image processing based on spatial domain and Hough transform is established, respectively. The original image of the nonwoven fabric mask is processed by region processing and edge extraction. Second, the defect detection algorithm of nonwoven fabric mask is established and the detection process is designed. Finally, a fast defect detection system for nonwoven fabric mask is designed, and the effectiveness of the detection method for nonwoven fabric mask is analyzed with an example. The results show that this detection method has positive engineering significance for improving the detection efficiency of defects in nonwoven fabric mask.

Application of machine vision technology in defect detection of high-performance phase noise measurement chips

The problem of chip defects has always existed in industrial production, and since there are more and more environmental problems caused by chip defects, people have attached greater importance to the identification and detection of chip defects. Pursuant to the ecological environmental problems caused by chip defects in the process of chip production, this paper uses machine vision technology to detect the defects of high-performance phase noise measurement chips. The results suggest that the accuracy of machine vision technology for the identification of chip defects reaches up to 98%. The production volume of organic waste gas decreases from 5968.0t/a to 4000t/a. The yield of organic wastewater decreases from 5496m3/d to 4600m3/d. The production amount of solid waste reduces from 8000t/a to 6500t/a. The aforementioned data all confirm that machine vision technology has the advantages of automation, high detection efficiency, and high accuracy of defect identification for the defect detection of high-performance phase noise measurement chips. And also, by improving the chip defects, the discharge volume of waste gas, wastewater, and solid waste in the chip production process is reduced, and thereupon the ecological environment is ameliorated.

An innovative application of pantograph recognition system based on deep learning

One of the most significant aspects for the correct operation of a modern railway electrification system is the health of the pantograph and overhead line. The application of machine vision technology to monitor the status of pantographs in real time can reduce pantographs and catenary accidents caused by unpredictable events. It is challenging to achieve real-time and accurate criteria with present pantograph detecting technologies. Therefore, this methodology collects pantograph images through high-definition cameras and transmits them to the cloud through 5G, use the Mask R-CNN algorithm to process and analyse the images., This technology can assist railway technicians in judging the status of the pantograph. Mask R-CNN employs the Resnet network for feature extraction. Resnet has the characteristics of cross-layer connection, which avoids the problem of network degradation due to the deep learning network being too deep, and greatly improves the training efficiency. The recognition matching degree of pantographs are greater than 0.975, enabling pantograph recognition in a variety of environmental conditions. The use of 5G connection increases transmission speed and allows for real-time detection of pantograph status, which is critical for the railway's automated operation.

Progress of Machine Vision Technologies in Intelligent Dairy Farming

The large-scale and precise intelligent breeding mode for dairy cows is the main direction for the development of the dairy industry. Machine vision has become an important technological means for the intelligent breeding of dairy cows due to its non-invasive, low-cost, and multi-behavior recognition capabilities. This review summarizes the recent application of machine vision technology, machine learning, and deep learning in the main behavior recognition of dairy cows. The authors summarized identity recognition technology based on facial features, muzzle prints, and body features of dairy cows; motion behavior recognition technology such as lying, standing, walking, drinking, eating, rumination, estrus; and the recognition of common diseases such as lameness and mastitis. Based on current research results, machine vision technology will become one of the important technological means for the intelligent breeding of dairy cows. Finally, the author also summarized the advantages of this technology in intelligent dairy farming, as well as the problems and challenges faced in the next development.

Research on Machine Vision Technology and its Application in Mechanical Manufacturing Automation

Machine vision technology was also an important part in the modern intelligent technology, as its cooperation with mechanical engineering automation could achieve technical complementation, promote the effective completion of mechanical automation production goals, and bring significant positive effects. In this paper, the machine vision technology was introduced, and the specific applications of machine vision technology in measurement, mechanical positioning, welding robot as well as other aspects of the mechanical manufacturing automation were analyzed. Meanwhile, the applications of machine vision technology in optical projection technology and agricultural production were listed. All of these not only proved that machine vision technology was an important basis for the optimization and development of mechanical manufacturing technology; but also provided some references for the integrated application of machine vision technology in mechanical manufacturing automation.

A Cascaded Individual Cow Identification Method Based on DeepOtsu and EfficientNet

Precision dairy farming technology is widely used to improve the management efficiency and reduce cost in large-scale dairy farms. Machine vision systems are non-contact technologies to obtain individual and behavioral information from animals. However, the accuracy of image-based individual identification of dairy cows is still inadequate, which limits the application of machine vision technologies in large-scale dairy farms. There are three key problems in dairy cattle identification based on images and biometrics: (1) the biometrics of different dairy cattle may be similar; (2) the complex shooting environment leads to the instability of image quality; and (3) for the end-to-end identification method, the identity of each cow corresponds to a pattern, and the increase in the number of cows will lead to a rapid increase in the number of outputs and parameters of the identification model. To solve the above problems, this paper proposes a cascaded dairy individual cow identification method based on DeepOtsu and EfficientNet, which can realize a breakthrough in dairy cow group identification accuracy and speed by binarization and cascaded classification of dairy cow body pattern images. The specific implementation steps of the proposed method are as follows. First, the YOLOX model was used to locate the trunk of the cow in the side-looking walking image to obtain the body pattern image, and then, the DeepOtsu model was used to binarize the body pattern image. After that, primary classification was carried out according to the proportion of black pixels in the binary image; then, for each subcategory obtained by the primary classification, the EfficientNet-B1 model was used for secondary classification to achieve accurate and rapid identification of dairy cows. A total of 11,800 side-looking walking images of 118 cows were used to construct the dataset; and the training set, validation set, and test set were constructed at a ratio of 5:3:2. The test results showed that the binarization segmentation accuracy of the body pattern image is 0.932, and the overall identification accuracy of the individual cow identification method is 0.985. The total processing time of a single image is 0.433 s. The proposed method outperforms the end-to-end dairy individual cow identification method in terms of efficiency and training speed. This study provides a new method for the identification of individual dairy cattle in large-scale dairy farms.

Fast detection of banana bunches and stalks in the natural environment based on deep learning

With the widespread application of machine vision technology in agriculture, the intelligent management of banana orchards is urgent. Accurate detection of banana bunches and stalks is a precondition for orchard yield estimation and automatic harvesting. In a complex banana orchard environment, banana bunches and stalks are similar to the leaves in color, and banana stalks are similar to the petiole in texture, making the detection of banana bunches and stalks in banana orchards challenging. This study proposes an accurate and fast multiclass detection method for banana bunches and stalks. A regular RGB camera was used to collect images. The well-known YOLOv4 network was used to detect the banana bunches and stalks, and the input image resolution was discussed by training and comparison. The banana bunch and stalk detection model showed excellent reliability and generalization ability in different illumination and occlusion scenarios. The AP of the banana bunch and stalk detection was 99.55% and 87.82%, respectively, and the mAP of the detection model was 93.69%. The average execution time was 44.96 ms. The detection of small-sized banana bunches and stalks was discussed, and its significance in banana orchard applications was analyzed. The experimental results show that the fast real-time detection of banana bunches and stalks in the natural environment is helpful for the intelligent management of banana orchards.

An Automatic Detection and Online Quality Inspection Method for Workpiece Surface Cracks based on Machine Vision

The wide application of intelligent manufacturing technologies imposes higher requirements for the quality inspection of industrial products; however, the existing industrial product quality inspection methods generally have a few shortcomings such as requiring many inspectors, too complicated methods, difficulty in realizing standardized monitoring, and the low inspection efficiency, etc. Targeting at these problems, this paper proposed an automatic detection and online quality inspection method for workpiece surface cracks based on the machine vision technology. At first, it proposed a vision-field environment calibration method, gave the specific method for workpiece shape feature recognition and size measurement based on machine vision, and achieved the on-line monitoring of workpiece quality problems such as feature defects and size deviations. Then, this study integrated the multi-scale attention module and the up-sampling module that can restore the locations of image pixels based on the high-level and low-level hybrid feature maps, built a workpiece crack extraction network, and realized workpiece crack feature extraction, crack type classification, and damage degree division. At last, experimental results verified the effectiveness of the proposed method, and this paper provided a reference for the application of machine vision technology in other fields.

Design of servo drive system for machine vision inspection platform

With the more application of machine vision technology in production practice, most machine vision systems are based on passive vision to measure the target, which has some limitations. Based on the requirements of machine vision platform, a three-dimensional servo movement scheme based on active positioning vision is proposed in this paper. In this paper, the parts of the servo drive system of the platform are selected, calculated and checked, the three-dimensional modeling of the machine vision platform is completed in SolidWorks, and the motion simulation of the servo control system is carried out.

Three-Dimensional Morphology and Size Measurement of High-Temperature Metal Components Based on Machine Vision Technology: A Review.

The three-dimensional (3D) size and morphology of high-temperature metal components need to be measured in real time during manufacturing processes, such as forging and rolling. Since the surface temperature of a metal component is very high during the forming and manufacturing process, manually measuring the size of a metal component at a close distance is difficult; hence, a non-contact measurement technology is required to complete the measurement. Recently, machine vision technology has been developed, which is a non-contact measurement technology that only needs to capture multiple images of a measured object to obtain the 3D size and morphology information, and this technology can be used in some extreme conditions. Machine vision technology has been widely used in industrial, agricultural, military and other fields, especially fields involving various high-temperature metal components. This paper provides a comprehensive review of the application of machine vision technology in measuring the 3D size and morphology of high-temperature metal components. Furthermore, according to the principle and method of measuring equipment structures, this review highlights two aspects in detail: laser scanning measurement and multi-view stereo vision technology. Special attention is paid to each method through comparisons and analyses to provide essential technical references for subsequent researchers.

Application of Machine Vision Technology in Garlic Planter

Mechanized garlic planting is a very necessary trend. At present, most garlic planters do not have the function of correcting garlic posture during the seeding process, which will greatly affect the growth of garlic seedlings [1]. This paper mainly describes how to recognize garlic orientation before garlic planting based on machine vision using Python language and LBP feature algorithm, and then correct it. This machine can accurately achieve the purpose of garlic clove scale buds facing upwards and increase the rate of positive buds.

A feasibility research on the application of machine vision technology in appearance quality inspection of Xuesaitong dropping pills

Defect detection is a critical issue for the quality control of dropping pills, which is a special dosage form of traditional Chinese Medicine. Machine vision is a non-destructing testing technology and cost-effective with high accuracy that can be used to predict the detects of both interior and exterior of the sample by employing the camera. In this research, a machine vision system for inspecting quality of the Xuesaitong dropping pills (XDPs) that include non-spherical, abnormal sizes and colors was developed to evaluate the appearance quality of XDPs rapidly and accurately. Firstly, 270 images of XDPs containing qualified and three different types of defects were collected. Subsequently, the processing of the XDPs images were carried out. Finally, Three defecting categories classification models were developed and compared based on contour and color features. The experimental results showed that the Random Forest outperformed all the explored models and the classification accuracy for non-spherical, abnormal sizes and colors reached 98.52%, 100.00% and 100.00%, respectively. In summary, the method established in this research is scientific, reliable, fast and accurate, which has great application potential and can provide technical support for the automatic defect detection of dropping pills.

Exploring the solutions via Retinex enhancements for fruit recognition impacts of outdoor sunlight: a case study of navel oranges

Machine vision-based techniques are one of the critical means to realize intelligent orchard management. Additionally, the third wave of artificial intelligence guided by deep learning has promoted the application of machine vision technology in fruit recognition. Currently, multiple detection models can extract the fruits from collected images, yet the accuracy has often existed deviation, due to different observation times cause corresponding changes of sunlight throughout a day. Consequently, exploring a method to solve this problem is of great significance to facilitating smart orchards. On this basis, this article takes the navel orange as a study object, dividing four observation periods (10:00–11:00, 12:00–13:00, 14:00–15:00, 16:00–17:00) and two viewing distances of one-meter and two-meter to collect image data. Corresponding algorithms are designed according to each Retinex processing modes to assist the detection of YOLOv5, including single-scale Retinex (SSR), multi-scale Retinex (MSR), multi-scale Retinex with color restoration (MSRCR), multi-scale Retinex with chromaticity preservation (MSRCP), and MSRCR with automatic color gradation adjustment (AutoMSRCR). The experimental results showed that two observation periods of 10:00–11:00 and 14:00–15:00 are more conducive to the data collection, where MSR-based and MSRCR-based models respectively from the two periods improved 9.28% and 6.32% of mean average precision (MAP) than original YOLOv5 under one-meter viewing distance. Also, MSRCR-based and AutoMSRCR-based models achieved 4.92% and 16.91% of MAP higher than the original under two-meter viewing distance. Simultaneously, this article also provides technical selection schemes and analyzes the sensitivity of each model in typical impact scenarios.