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ردیف | عنوان | نوع |
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1 |
Deep convolutional neural networks-based Hardware–Software on-chip system for computer vision application
سیستم سختافزار-نرمافزار روی تراشه مبتنی بر شبکههای عصبی عمیق برای کاربرد بینایی ماشین-2022 Embedded vision systems are the best solutions for high-performance and lightning-fast inspection tasks. As everyday life evolves, it becomes almost imperative to harness artificial
intelligence (AI) in vision applications that make these systems intelligent and able to make
decisions close to or similar to humans. In this context, the AI’s integration on embedded
systems poses many challenges, given that its performance depends on data volume and
quality they assimilate to learn and improve. This returns to the energy consumption and
cost constraints of the FPGA-SoC that have limited processing, memory, and communication
capacity. Despite this, the AI algorithm implementation on embedded systems can drastically
reduce energy consumption and processing times, while reducing the costs and risks associated
with data transmission. Therefore, its efficiency and reliability always depend on the designed
prototypes. Within this range, this work proposes two different designs for the Traffic Sign
Recognition (TSR) application based on the convolutional neural network (CNN) model,
followed by three implantations on PYNQ-Z1. Firstly, we propose to implement the CNN-based
TSR application on the PYNQ-Z1 processor. Considering its runtime result of around 3.55 s,
there is room for improvement using programmable logic (PL) and processing system (PS) in a
hybrid architecture. Therefore, we propose a streaming architecture, in which the CNN layers
will be accelerated to provide a hardware accelerator for each layer where direct memory
access (DMA) interface is used. Thus, we noticed efficient power consumption, decreased
hardware cost, and execution time optimization of 2.13 s, but, there was still room for design
optimizations. Finally, we propose a second co-design, in which the CNN will be accelerated
to be a single computation engine where BRAM interface is used. The implementation results
prove that our proposed embedded TSR design achieves the best performances compared to the
first proposed architectures, in terms of execution time of about 0.03 s, computation roof of
about 36.6 GFLOPS, and bandwidth roof of about 3.2 GByte/s.
keywords: CNN | FPGA | Acceleration | Co-design | PYNQ-Z1 |
مقاله انگلیسی |
2 |
Tuning of grayscale computer vision systems
تنظیم سیستم های بینایی کامپیوتری در مقیاس خاکستری-2022 Computer vision systems perform based on their design and parameter setting. In computer vision systems
that use grayscale conversion, the conversion of RGB images to a grayscale format influences performance of
the systems in terms of both results quality and computational costs. Appropriate setting of the weights for
the weighted means grayscale conversion, co-estimated with other parameters used in the computer vision
system, helps to approach the desired performance of a system or its subsystem at the cost of a negligible or
no increase in its time-complexity. However, parameter space of the system and subsystem as extended by the
grayscale conversion weights can contain substandard settings. These settings show strong sensitivity of the
system and subsystem to small changes in the distribution of data in a color space of the processed images.
We developed a methodology for Tuning of the Grayscale computer Vision systems (TGV) that exploits the
advantages while compensating for the disadvantages of the weighted means grayscale conversion. We show
that the TGV tuning improves computer vision system performance by up to 16% in the tested case studies.
The methodology provides a universally applicable solution that merges the utility of a fine-tuned computer
vision system with the robustness of its performance against variable input data.
keywords: Computer vision | Parameter optimization | Performance evaluation | WECIA graph | Weighted means grayscale conversion |
مقاله انگلیسی |
3 |
Human perception of color differences using computer vision system measurements of raw pork loin
درک انسان از تفاوتهای رنگی با استفاده از اندازهگیریهای سیستم بینایی کامپیوتری گوشت خوک خام-2022 In the food industry, product color plays an important role in influencing consumer choices. Yet, there remains
little research on the human ability to perceive differences in product color; therefore, preference testing is
subjective rather than based on quantitative colors. Using a de-centralized computer-aided systematic discrim-
ination testing method, we ascertain consumers’ ability to discern between systematically varied colors. As a case
study, the colors represent the color variability of fresh pork as measured by a computer vision system. Our
results indicate that a total color difference (ΔE) of approximately 1 is discriminable by consumers. Furthermore,
we ascertain that a change in color along the b*-axis (yellowness) in CIELAB color space is most discernable,
followed by the a*-axis (redness) and then the L*-axis (lightness). As developed, our web-based discrimination
testing approach allows for large scale evaluation of human color perception, while these quantitative findings
on meat color discrimination are of value for future research on consumer preferences of meat color and beyond. keywords: تست تبعیض | تست مثلث | ترجیح رنگ | ظاهر غذا | رنگ گوشت | Discrimination testing | Triange test | Color preference | Food appearance | Meat color |
مقاله انگلیسی |
4 |
The application of computer vision systems in meat science and industry – A review
کاربرد سیستم های بینایی کامپیوتری در علم و صنعت گوشت – مروری-2022 Computer vision systems (CVS) are applied to macro- and microscopic digital photographs captured using digital
cameras, ultrasound scanners, computer tomography, and wide-angle imaging cameras. Diverse image acquisi-
tion devices make it technically feasible to obtain information about both the external features and internal
structures of targeted objects. Attributes measured in CVS can be used to evaluate meat quality. CVS are also used
in research related to assessing the composition of animal carcasses, which might help determine the impact of
cross-breeding or rearing systems on the quality of meat. The results obtained by the CVS technique also
contribute to assessing the impact of technological treatments on the quality of raw and cooked meat. CVS have
many positive attributes including objectivity, non-invasiveness, speed, and low cost of analysis and systems are
under constant development an improvement. The present review covers computer vision system techniques,
stages of measurements, and possibilities for using these to assess carcass and meat quality. keywords: سیستم بینایی کامپیوتری | گوشت | محصولات گوشتی | لاشه | Computer vision system | Meat | Meat products | Carcass |
مقاله انگلیسی |
5 |
Non-destructive and contactless estimation of chlorophyll and ammonia contents in packaged fresh-cut rocket leaves by a Computer Vision System
تخمین غیر مخرب و بدون تماس محتویات کلروفیل و آمونیاک در برگ های موشک تازه برش خورده بسته بندی شده توسط یک سیستم کامپیوتر ویژن-2022 Computer Vision Systems (CVS) offer a non-destructive and contactless tool to assign visual quality level to fruit
and vegetables and to estimate some of their internal characteristics. The innovative CVS described in this paper
exploits the combination of image processing techniques and machine learning models (Random Forests) to
assess the visual quality and predict the internal traits on unpackaged and packaged rocket leaves. Its perfor-
mance did not depend on the cultivation system (traditional soil or soilless). The same CVS, exploiting its ma-
chine learning components, was able to build effective models for either the classification problem (visual quality
level assignment) and the regression problems (estimation of senescence indicators such as chlorophyll and
ammonia contents) just by changing the training data. The experiments showed a negligible performance loss on
packaged products (Pearson’s linear correlation coefficient of 0.84 for chlorophyll and 0.91 for ammonia) with
respect to unpackaged ones (0.86 for chlorophyll and 0.92 for ammonia). Thus, the non-destructive and con-
tactless CVS represents a valid alternative to destructive, expensive and time-consuming analyses in the lab and
can be effectively and extensively used along the whole supply chain, even on packaged products that cannot be
analyzed using traditional tools. keywords: Contactless quality level assessment | Diplotaxis tenuifolia L | Image analysis | Packaged vegetables | Senescence indicators prediction |
مقاله انگلیسی |
6 |
A computer vision system for early detection of anthracnose in sugar mango (Mangifera indica) based on UV-A illumination
یک سیستم بینایی کامپیوتری برای تشخیص زودهنگام آنتراکنوز در انبه قندی (Mangifera indica) بر اساس نور UV-A-2022 The present work describes the development of a computer vision system for the early detection of anthracnose in sugar mango based on Ultraviolet A illumination (UV-A). Anthracnose, a disease caused by the fungus Colletotrichum sp, is commonly found in the fruit of sugar mango (Mangifera indica). It manifests as surface defects including black spots and is responsible for reducing the quality of the fruit. Consequently, it decreases its commercial value. In more detail, this study poses a system that begins with image acquisition under white and ultraviolet illumination. Furthermore, it proposes to analyze the Red, Green and Blue color information (R, G, B) of the pixels under two types of illumination, using four different methods: RGB-threshold, RGB-Linear Discriminant Analysis (RGB-LDA), UV-LDA, and UV-threshold. This analysis produces an early semantic segmentation of healthy and diseased areas of the mango image. The results showed that the combination of the linear discriminant analysis (LDA) and UV-A light (called UV-LDA method) in sugar mango images allows early detection of anthracnose. Particularly, this method achieves the identification of the disease one day earlier than by an expert with respect to the scale of anthracnose severity implemented in this work.
keywords: انبه قندی | آنتراکنوز | LDA | نور UV-A | درجه بندی | پردازش تصویر | Sugar mango | Anthracnose | LDA | UV-A light | Grading | Image processing |
مقاله انگلیسی |
7 |
Evaluating Congou black tea quality using a lab-made computer vision system coupled with morphological features and chemometrics
ارزیابی کیفیت چای سیاه کنگو با استفاده از یک سیستم بینایی کامپیوتری ساخته شده در آزمایشگاه همراه با ویژگی های مورفولوژیکی و شیمی سنجی-2021 The feature of external shape in tea is a vital quality index that determines the rank quality of tea. The potential of a lab-made computer vision system (CVS) coupled with morphological features and chemometric tools is investigated for evaluating Congou black tea quality. First, Raw images of 700 tea samples from seven different quality grades are acquired using the CVS. The original images collected are processed by graying, binarization, and median de-noising. Then, six morphological parameters (viz. width, length, area, perimeter, length-width ratio, and rectangularity) from the samples are extracted by the shape segmentation of each tea leaf image, and the corresponding feature histogram is obtained. Finally, support vector machine (SVM) and least squares- support vector machine (LS-SVM) are utilized to build identification models based on the histogram distribution characteristic vectors. Three kernel methods (linear kernel, polynomial kernel, and radial basis function kernel) are compared for monitoring tea quality. The results show that the optimal LS-SVM model has a 12% higher correct discrimination rate (CDR) than the SVM model. The polynomial kernel LS-SVM model yields satisfactory classification results with the CDR of 100% based on selected six shape features in the calibration and prediction sets. This work demonstrates that it is feasible to discriminate Congou black tea quality using CVS technology along with morphological features and nonlinear chemometric methods. A new perspective on the sizes of morphological characteristics is proposed as an identifier of Congou black tea quality. Keywords: Congou black tea | Computer vision system | Morphological features | Least squares-support vector machine | Kernel method |
مقاله انگلیسی |
8 |
A high performance real-time vision system for curved surface inspection
یک سیستم دید در زمان واقعی با عملکرد بالا برای بازرسی سطح منحنی-2021 Surface quality plays an important role in inspection lines. In this paper, a novel imaging device combined with FPGA (Field Programmable Gate Array) based processing platform had been designed to detect and analyze curved surface defects for vision inspection. The optical imaging part was made by an optical device which can be used to collect curved surface features without anamorphous and a camera with 70k Hz linear CMOS was used to capture surface information. The FPGA based inspection platform had been developed for camera control and image processing. Inspecting experiments had been tested with an inspection accuracy of 0.2 mm x 0.2 mm which satisfied a 12 m/s real-time vision inspection line. This research result can be subsequently applied to various surface inspection scenarios. Keywords: Optical imaging | Curved surface inspection | Vision system | Image processing |
مقاله انگلیسی |
9 |
Weight and volume estimation of poultry and products based on computer vision systems: a review
Weight and volume estimation of poultry and products based on computer vision systems: a review-2021 The appearance, size, and weight of
poultry meat and eggs are essential for production economics and vital in the poultry sector. These external
characteristics influence their market price and consumers’ preference and choice. With technological developments, there is an increase in the application and
importance of vision systems in the agricultural sector.
Computer vision has become a promising tool in the realtime automation of poultry weighing and processing
systems. Owing to its noninvasive and nonintrusive nature and its capacity to present a wide range of information, computer vision systems can be applied in the
size, mass, volume determination, and sorting and
grading of poultry products. This review article gives a
detailed summary of the current advances in measuring
poultry products’ external characteristics based on
computer vision systems. An overview of computer
vision systems is discussed and summarized. A comprehensive presentation of the application of computer
vision-based systems for assessing poultry meat and eggs
was provided, that is, weight and volume estimation,
sorting, and classification. Finally, the challenges and
potential future trends in size, weight, and volume estimation of poultry products are reported.
Key words: classification | computer vision | egg | weight estimation | poultry product |
مقاله انگلیسی |
10 |
Field-programmable gate arrays in a low power vision system
آرایه های دروازه ای قابل برنامه ریزی در یک سیستم دید کم قدرت-2021 In recent years, field-programmable gate arrays have played a major role in developing low power electronic systems. End users usually prefer systems with high performance, reduced size, and low power consumption. These requirements create a challenging task for designers. Re-configuring technology allows the use of field-programmable gate arrays to be at the maximum level during runtime. This paper proposes the implementation of the Dynamic Partial Reconfiguration technique to switch during runtime between two edge detection algorithms (FASTX and Sobel) in a computer vision algorithm. Xilinx Ultrascale+ZCU106 has been used as the implementation target since it consumes approximately 4% less power during runtime. It was discovered that the dynamic switching between algorithms reduces the on-chip area utilization. Finally, through experimental results our proposed work has demonstrated the applicability of computer vision with low power consumption. Keywords: Ultrascale | Low power | Computer vision application | Dynamic partial reconfiguration |
مقاله انگلیسی |