In order to preserve corn quality it is important to obtain physical properties and assess mechanical damage so as to design optimum handling and storage equipment. Measurements of kernel length, width and projected area independent of kernel orientation have been performed using machine vision Ni et al. Ng et al.
Deep Learning Vs. Machine Vision and Human Inspection
They found that this method was more consistent than other methods available. The automatic inspection of corn kernels was also performed by Ni et al. In other studies Xie and Paulsen used machine vision to detect and quantify tetrazolium staining in corn kernels. The tetrazolium-machine vision algorithm was used to predict heat damage in corn due to drying air temperature and initial moisture content. Conclusively it could be say that the machine vision system is playing a versatile role in quality evaluation corn so for.
As rice is one of the leading food crops of the world its quality evaluation is of importance to ensure it remains appealing to consumers. Liu et al. Wan et al. The range selection method achieved this accuracy but required time-consuming and complicated adjustment. In another study, milled rice from a laboratory mill and a commercial-scale mill was evaluated for head rice yield and percentage whole kernels, using a shaker table and a machine-vision system called the Grain Check Lloyd et al.
However, a flat bed scanner was used in machine vision system developed by Shahin and Symons for colour grading of lentils. In their study, they scanned and analyzed different grades of large green lentils over a two-crop season period and developed an online classification system based on neural classifier. Visual features such as colour and size indicate the quality of many prepared consumer foods. Sun investigated this in research on pizza in which pizza topping percentage and distribution were extracted from pizza images.
To avoid the missguideness of quality assessment by visual based human perception, computer vision has been widely used in the assessment of confectionary products so far. Davidson et al. Four fuzzy models were developed to predict consumer ratings based on three of the features. A prototype-automated system for visual inspection of muffins was developed by Abdullah et al. Now, the machine vision system has also been used in the assessment of quality of crumb grain in bread and cake products Sapirstein The evaluation of the functional properties of cheese is assessed to ensure the necessary quality is achieved, especially for specialized applications such as consumer food toppings or ingredients.
Wang and Sun developed a computer vision method to evaluate the melting and browning of cheese. This novel non-contact method was employed to analyze the characteristics of cheddar and mozzarella cheeses during cooking and the results showed that the method provided an objective and easy approach for analyzing cheese functional properties Wang and Sun a , b. Ni and Gunasekaran developed an image-processing algorithm to recognize individual cheese shred and automatically measure the shred length.
It was found that the algorithm recognized shreds well, even when they were overlapping. It was also reported that the shred length measurement errors were as low as 0. The majority of Asian noodle manufacturers noodle products to be derived from white seed-coated wheat as compared to red seed-coat material. Initial research had used image analysis to characterize the white wheat products Hatcher and Symons b.
The standard methodology to assess bran contamination has been ash content, the use of auto fluorescence by different wheat seed tissue Munck et al. Hatcher demonstrated that the image analysis could be effectively used to quantify, measure, and discriminate varietal differences in white seed coated wheat varieties in preparation of yellow alkaline noodles prepared from high-quality patent flour. The images of commercial potato chips were evaluated for various colour Brosnan and Sun and textural features to characterize and classify the appearance Pedreschi et al.
The most frequently used color model is the RGB model, in which each sensor captures the intensity of the light in the red R , green G and blue B spectra respectively, for food quality evaluation Du and Sun However, the features derived from the image texture contained better information than colour features to discriminate both the quality categories of chips and consumers preferences. Pedreschi et al. Leon et al. After the evaluation of the performance of the models, the neural network model was found to perform the best. In another study, a stepwise logistic regression model was developed by Mendoza et al.
Visually discernible characteristics are routinely used in the quality assessment of meat. McDonald and Chen pioneered early work in the area of image based beef grading. Based on reflectance characteristics, they discriminated between fat and lean in the Longisimus muscle and generated binary muscle images. In a more recent study Gerrard et al. Image texture analysis has also been used in the assessment of beef tenderness Li et al. Statistic regression and neural network were performed to compare the image features and sensory scores for beef tenderness and it was found that the texture features considerably contributed to the beef tenderness.
In another study by Lu et al. Furthermore, Gray-scale intensity, Fourier power spectrum, and fractal analyses were used as a basis for separating tumorous, bruised and skin torn chicken carcasses from normal carcasses Park et al. In a further study Park and Chen found that a linear discriminant model was able to identify unwholesome chicken carcasses with classification accuracy of Daley et al.
Although there are several factors that affect the perception of taste, tenderness is considered the most important characteristic. Cortez and Portelinha presented, a feature selection procedure, based on a Sensitivity Analysis, is combined with a Support Vector Machine, in order to predict lamb meat tenderness.
This real-world problem is defined in terms of two difficult regression tasks, by modeling objective e. Warner-Bratzler Shear force and subjective e. In both cases, the proposed solution is competitive when compared with other neural e. Multilayer Perceptron and Multiple Regression approaches. Machine vision system is seen as an easy and quick way to acquire data that would be otherwise difficult to obtain manually Lefebvre et al. Since, the capabilities of digital image analysis technology to generate precise descriptive data on pictorial information have contributed to its more widespread and increased use Sapirstein Quality control in combination with the increasing automation in all fields of production has led to the increased demand for automatic and objective evaluation of different products.
Sistler confirm that computer vision meets these criteria and states that the technique provides a quick and objective means for measuring visual features of products. In agreement it found that a computer vision system with an automatic handling mechanism could perform inspections objectively and reduce tedious human involvement Morrow et al. In other study, Gerrard et al. Another benefit of machine vision systems is the non-destructive and undisturbing manner in which information could be attained Zayas et al.
Tarbell and Reid noted that an attractive feature of a machine vision system is that it can be used to create a permanent record of any measurement at any point in time. Hence archived images can be recalled to look at attributes that were missed or previously not of interest. Human grader inspection and grading of produce is often a labour intensive, tedious, repetitive and subjective task Park et al. In addition to its costs, this method is variable and decisions are not always consistent between inspectors or from day to day Tao et al.
In contrast Lu et al. Hence computer vision could be used widely in agricultural and horticulture to automate many labour intensive processes Gunasekaran An ambiguity of computer vision is that its results are influenced by the quality of the captured images. Often due to the unstructured nature of typical agricultural settings and biological variation of plants within them, object identification in these applications is considerably more difficult.
Also if the research or operation in being conducted in dim or night conditions artificial lighting is needed. So, according to above findings, computer vision has been widely used for the sorting and grading of fruits and vegetable. It offers potential to automate manual grading practices and thus to standardize techniques and eliminate tedious inspection tasks.
The paper on machine vision system reviews the recent developments in computer vision for the agricultural and food industry. Machine vision systems have been used increasingly in industry for inspection and evaluation purposes as they can provide rapid, economic, hygienic, consistent and objective assessment. However, there are some difficulties still exist in the adaptation of machine vision system, evident from the relatively slow commercial uptake of machine vision technology in all sectors.
In spite of this, with the advent of machine vision technology, a revolution has come in the field of automation. Automating an operation in a manufacturing plant requires a high degree of pre-installation systems engineering and post-installation process integration. The use of machine vision technology in manufacturing can be as simple as producing an inspection quality report or as complex as total process automation. At the end, we can say that machine vision is a powerful tool of automation that includes both image processing and image analysis tools.
PMID: Krishna Kumar Patel , A. Kar , S. Jha , and M. Krishna Kumar Patel, Email: ni. Corresponding author. Revised Jan 28; Accepted Feb 3. This article has been cited by other articles in PMC. Abstract Quality inspection of food and agricultural produce are difficult and labor intensive.
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Keywords: Machine vision, Image processing, Image analysis, Quality inspection, Food and agricultural products. Application areas Purpose Industrial automation and image processing Process control, quality control, geometrical measurement Barcode and package label reading, object sorting Parts identification on assembly lines, defect and fault inspection Inspection of printed circuit boards and integrated circuits Medical image analysis Tumor detection, measurement of size and shape of internal organs, blood cell count X-ray inspection Robotics Obstacle avoidance by recognition and interpretation of objects in a scene Collision avoidance, machining monitoring Hazard determination Radar imaging Target detection and identification, guidance of helicopters and aircrafts in landing, guidance of remote piloted vehicles RPV , guiding missiles and satellites from visual cues Food industry Sorting of vegetables and fruits, location of defects e.
Open in a separate window. Singh et al. Principle components of computer vision A computer vision system generally consists of five basic components: illumination, a camera, an image capture board frame grabber or digitizer , computer hardware and software as shown in Fig. Image processing and analysis The image processing and image analysis are the core of computer vision with numerous algorithms and methods available to achieve the required classification and measurements Krutz et al.
In spite of above, the basic machine vision and image processing algorithms can be divided in five major groups as: 1 Segmentation and algorithm development 2 Edge-detection techniques 3 Digital morphology 4 Texture and 5 Thinning and skeletonization algorithms Russ Segmentation and algorithm development.
Segmentation is typically used to locate objects and boundaries lines curves etc. More precisely it is the process of assigning a label to every pixel in an image. There are four main methods or algorithms for the selection of the global threshold: manual selection, isodata algorithm, objective function, and histogram clustering Zheng and Sun There are many other thresholding based segmentation techniques such as, the minimum error technique Kittler and Illingworth , the moment preserving technique Tsai , the window extension method Hwang et al.
And some important segmentation techniques are given in Fig. Although, a large number of segmentation techniques have been developed to date, no universal method can perform with the ideal efficiency and accuracy the infinity diversity of imagery Bhanu et al.
Therefore, it is expected that several techniques will need to be combined in order to improve the segmentation results and increase the adaptability of the methods. Furthermore, Li and Wang have developed a method based on reference image of apple to accomplish defect segmentation for curved fruits. An algorithm using colour information was developed by Leemans et al. Since the algorithm is a set of well-defined rules or procedures for solving a problem in a finite number of steps. The algorithm developed for the surface defect detection mainly includes modules of image preprocessing, defect segmentation, stem-calyx recognition, and defect area calculation and grading.
Forbes and Tattersfield developed a machine vision algorithm using neural networks and the algorithm was tested on the estimation of pear fruit volume from two-dimensional digital images. Another imaging algorithm was developed by Hahn and Sanchez to measure the volume of non-circular shaped agricultural produce, such as carrots. Using these methods, Wang and Nguang and Sabliov et al.
Li et al. Edge detection is an essential tool for machine vision and image processing. In image processing, an edge is the boundary between an object and its background. They represent the frontier for single objects. It is also the process of locating edge pixels and increasing the contrast between the edges and the background i. In addition, edge tracing is another terminology used by researcher, includes the process of following the edges, usually collecting the edge pixels into a list Parker Some of the well-known edge detectors that have been widely used are the Sobel, Prewitt, Roberts, and Kirsch detectors Russ The first quantitative measurements of the performance of edge detectors, including the assessment of the optimal signal-to-noise ratio and the optimal locality, the maximum suppression of false response, were performed by Canny , who also proposed an edge detector taking into account all three of these measurements.
The Canny edge detector was used in the food industry for boundary extraction of food products Du and Sun , b. Digital morphology is a group of mathematical operations that can be applied to the set of pixels to enhance or highlight specific aspects of the shape so that they can be counted or recognized Parker In morphological processing, images are represented as topographical surfaces on which the elevation of each point is assigned as the intensity value of the corresponding pixels Vincent and Soille One such method was proposed by Du and Sun a to segment pores in pork ham images.
In other methods, post processing is conducted to merge the over segmented regions with similar image characteristics together again. Such a method with a graphic algorithm to determine the similarity of merging neighbouring regions was developed by Navon et al. Texture effectively describes the properties of elements constituting the object surface, thus the texture measurements are believed to contain substantial information for the pattern recognition of objects Amadasun and King The repetition of a pattern or patterns over a region is called texture.
This pattern may be repeated exactly, or as set or small variations. Texture has a conflictive random aspect: the size, shape, color, and orientation of the elements of the pattern textons Parker Although texture can be roughly defined as the combination of some innate image properties, including fineness, coarseness, smoothness, granulation, randomness, lineation, hummocky, etc. Accordingly, there is no ideal method for measuring textures. Nevertheless, a great number of methods have been developed, and these are categorized into statistical, structural, transform-based, and model-based methods Zheng et al.
These methods capture texture measurements in two different ways—by the variation of across pixels and their neighbouring pixels Bharti et al. An image skeleton is a powerful analog concept that may be employed for the analysis and description of shapes in binary images. It plays a central role in the pre-processing of image data Gunasekaran a , b. A comprehensive review on thinning methodologies has been presented by Lam et al. In general, a skeleton may be defined as a connected set of medial lines along the limbs of a figure and skeletonization is a process to describe the global properties of an object and to reduce the original image into a more compact representation.
A basic method for skeletonization is thinning Parker Ni and Gunasekaran have applied a sequential thinning algorithm for evaluating cheese shred morphology when they are touching and overlapping. Naccache and Shinghal compared the results of 14 skeletonization algorithm and Davies deduced the ideal shape after analysed the skeleton shape of object. Furthermore, image processing can be done independently by image acquisition software Lichtenthaler et al.
Mehl et al. Bailey et al. Koc determined the volume of watermelons and Rashidi et al. The surface area and volume of asymmetric agricultural products were measured by Sabliov et al. Image processing algorithms are the basis for mmachine vision Martin and Tosunoglu and the image algebra Ritter and Wilson forms a solid theoretical foundation to implement computer vision and image processing algorithms.
A quadratic discriminant model based on an algorithm was developed by Harrell for consultation during on-line sorting. Image analysis uses digital data to display images and to mathematically manipulate images to highlight various characteristics. Areas of homogeneous color or edges between different color areas are examples of useful image features. Often, filtering of the original data can help to eliminate image noise, such as shading caused by uneven lighting.
Then, statistical and mathematical techniques are applied to the data to distinguish elements of the image Ballard and Brown Various operations and techniques are applied to the processed image to extract the desired information. Among these operations and techniques are: Object recognition; Feature extraction; Analysis of position, size, orientation, etc. Product type Product Application Accuracy Ref. Texture Features Expression Measure of Texture Average Gray Level A measure of average intensity Standard Deviation A measure of average contrast Smoothness Measures the relative smoothness of the intensity in a region f3 is 0 for a region of constant intensity and approaches 1 for regions with large excursions in the values of its intensity levels.
Skewness Measures the skewness of an histogram. This measure is 0 for symmetric histograms, positive by histograms skewed to the right and negative for histograms skewed to the left Uniformity Measures uniformity. This measure is maximum when all gray levels are equal maximal uniform and decreases from there. Entropy A measure of randomness. Applications Assessment of fruits Computer vision has been widely used for the inspection and grading of fruits. Assessment of vegetables Computer vision has been shown to be a viable approach to inspection and grading of vegetables Shearer and Payne a , b.
For grain classification and quality evaluation Wheat Grain quality attributes are very important for all users and especially the milling and baking industries. Corn In order to preserve corn quality it is important to obtain physical properties and assess mechanical damage so as to design optimum handling and storage equipment. Rice and lentils As rice is one of the leading food crops of the world its quality evaluation is of importance to ensure it remains appealing to consumers.
Processed food products Visual features such as colour and size indicate the quality of many prepared consumer foods. Cheese The evaluation of the functional properties of cheese is assessed to ensure the necessary quality is achieved, especially for specialized applications such as consumer food toppings or ingredients. Potato chips and French fries The images of commercial potato chips were evaluated for various colour Brosnan and Sun and textural features to characterize and classify the appearance Pedreschi et al.
Meat and meat products Visually discernible characteristics are routinely used in the quality assessment of meat. Advantages and disadvantages Machine vision system is seen as an easy and quick way to acquire data that would be otherwise difficult to obtain manually Lefebvre et al. Reference Advantages Generation of precise descriptive data Sapirstein Quick and objective Li et al. Brosnan and Sun Conclusion The paper on machine vision system reviews the recent developments in computer vision for the agricultural and food industry. Quality inspection of bakery products using color based machine vision system.
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WJ Machine Vision – We are expert in PC based
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Colour and image texture analysis in classification of commercial potato chips. A colour vision system for peach grading. Miskelly DM. Noodles-a new look at an old food. Food Aust. Molto E, Blasco J Quality evaluation of citrus fruits. Pattern recognition of fruit shape based on the concept of chaos and neural networks. Analysis of botanical components in cereals and cereals products: a new way of understanding cereal processing.
It is now making its way into the area of Production and Manufacturing, allowing it to harness the power of deep learning and in doing so, providing automation that is faster, cheaper and more superior. This article aims to give a brief understanding of automated visual assessment and how a deep learning approach can save significant time and effort. It involves the analysis of products on the production line for the purpose of quality control. Visual inspection can also be used for internal and external assessment of the various equipment in a production facility such as storage tanks, pressure vessels, piping, and other equipment.
It is a process that takes place at regular intervals of time, such as day-to-day. It has been shown repeatedly that visual inspection results in the discovery of most hidden defects during production. Among the many industries where visual inspection is required, there are several where visual inspection is considered to be of very high consequence and is high priority activity due to the potentially high cost of any errors that may arise via inspection such as injury, fatality, loss of expensive equipment, scrapped items, rework, or a loss of customers.
Such fields where visual inspection is prioritised include nuclear weapons, nuclear power, airport baggage screening, aircraft maintenance, food industry, medicine and pharmaceuticals.
While old might be gold, one could argue that there are several limitations to using the old fashioned way of inspection. Manual inspection requires the presence of a person, an inspector who performs assessment of the entity under question and passes judgement on it according to some training or previous knowledge. No equipment is required except the naked eye of the trained inspector. Some imperfections can be attributed to human error, while others are due to limitations of space. Certain errors can be reduced through training and practice, but cannot be completely removed.
Misses tend to occur much more frequently than false alarms See, Misses can lead to loss in quality, while false positives can cause unnecessary production costs and overall wastage. This does not necessarily mean that manual inspection is totally useless, but that it would be unwise to depend entirely on it.
Imprecision of eyesight — The human eye is incapable of making precise measurements, especially on a very tiny scale. Even while comparing two similar objects, the eye might not notice that one is slightly smaller or larger than the other. This concept also applies to characteristics such as surface roughness, size, and any other factor that needs to be measured.
Cost of labour — Manual inspection remains a costly venture due to the appointment of multiple trained individuals. Automated Visual inspection can overcome these problems by making the whole procedure of visual inspection independent of any human involvement. Using automated systems typically surpasses the standard of manual inspection. Using deep learning and machine vision, it is not only possible but quite achievable to build smart systems that perform thorough quality checks down to the finest details. Minimal physical equipment is needed to automate the visual inspection process.
Instead the process is made smarter due to the use of Deep Learning. This approach typically involves steps such as image acquisition, preprocessing, feature extraction, classification etc. Deep learning technology uses neural networks containing thousands of layers which are adept at mimicking human level intelligence to distinguish anomalies, parts, and characters while tolerating natural variations in complex patterns.
In this way, deep learning merges the adaptability of human visual inspection with the speed and robustness of a computerised system. Deep learning teaches machines to do what comes naturally to humans: to learn by example. This gives manufacturing technology amazing new abilities to recognize images, distinguish trends, and make intelligent predictions and decisions. Starting from a core logic developed during initial training, deep neural networks can continuously refine their performance as they are presented with new images, speech, and text.
Machine vision is the technology and methods used to provide image-based automatic inspection. The components of an automatic inspection system usually include lighting, a camera or other image acquiring device, a processor, software, and output devices. Machine vision surpasses human vision at the quantitative and qualitative measurement of a structured scene because of its speed, accuracy, and repeatability.
A machine vision system can easily assess object details too small to be seen by the human eye, and inspect them with greater reliability and lesser error. On a production line, machine vision systems can inspect hundreds or thousands of parts per minute reliably and repeatedly, far exceeding the inspection capabilities of humans.
A traditional automated system, while minimising costs and improving efficiency does not have the flexibility or tolerance for variation that human beings do. Manual inspectors are able to distinguish between subtle, cosmetic and functional flaws, and can interpret variations in part appearance that may affect perceived quality. Though limited in the rate at which they can process information, humans are uniquely able to conceptualise and generalize.
Humans excel at learning by example and can differentiate what really matters when it comes to slight anomalies between parts. Machine vision systems alone fail to assess the vast possibility of variation and deviation between very visually similar images. Deep learning-based systems are well-suited for visual inspections that are more complex in nature: patterns that vary in subtle but tolerable ways.
Deep learning is good at addressing complex surface and cosmetic defects, like scratches and dents on parts that are turned, brushed, or shiny. Machine Vision has a very high optical resolution which depends upon the technology and equipment used for image acquisition. The system has all the power associated with higher processing speeds along with a potentially infinite memory capacity. In terms of requirements, AVI does not really require much physical equipment. The equipment needed to start automating visual inspection can be split into hardware and software resources. These resources consist of primary equipments such as a camera, photometer, colorimeter and optional secondary equipment such as required for grading or sorting, which would be dependent on industry and automation processes.
Depending on the industry where it being use, the physical equipment can actually be categorized into three subsystems.