با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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1 |
High-accuracy in the classification of butchery cut marks and crocodile tooth marks using machine learning methods and computer vision algorithms
دقت بالا در طبقه بندی علائم برش قصابی و علائم دندان تمساح با استفاده از روش های یادگیری ماشین و الگوریتم های بینایی کامپیوتری-2022 Some researchers using traditional taphonomic criteria (groove shape and presence/absence of microstriations) have cast some doubts about the potential equifinality presented by crocodile tooth marks and
stone tool butchery cut marks. Other researchers have argued that multivariate methods can efficiently
separate both types of marks. Differentiating both taphonomic agents is crucial for determining the earliest evidence of carcass processing by hominins. Here, we use an updated machine learning approach
(discarding artificially bootstrapping the original imbalanced samples) to show that microscopic features
shaped as categorical variables, corresponding to intrinsic properties of mark structure, can accurately
discriminate both types of bone modifications. We also implement new deep-learning methods that
objectively achieve the highest accuracy in differentiating cut marks from crocodile tooth scores (99%
of testing sets). The present study shows that there are precise ways of differentiating both taphonomic
agents, and this invites taphonomists to apply them to controversial paleontological and archaeological
specimens.
keywords: تافونومی | علائم برش | علائم دندان | فراگیری ماشین | یادگیری عمیق | شبکه های عصبی کانولوشنال | قصابی | Taphonomy | Cut marks | Tooth marks | Machine learning | Deep learning | Convolutional neural networks | Butchery |
مقاله انگلیسی |
2 |
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 |
3 |
ChickenNet - an end-to-end approach for plumage condition assessment of laying hens in commercial farms using computer vision
ChickenNet - یک رویکرد انتها به انتها برای ارزیابی وضعیت پرهای مرغ های تخمگذار در مزارع تجاری با استفاده از بینایی کامپیوتر-2022 Regular plumage condition assessment in laying hens is essential to monitor the hens’ welfare status and to
detect the occurrence of feather pecking activities. However, in commercial farms this is a labor-intensive,
manual task. This study proposes a novel approach for automated plumage condition assessment using com-
puter vision and deep learning. It presents ChickenNet, an end-to-end convolutional neural network that detects
hens and simultaneously predicts a plumage condition score for each detected hen. To investigate the effect of
input image characteristics, the method was evaluated using images with and without depth information in
resolutions of 384 × 384, 512 × 512, 896 × 896 and 1216 × 1216 pixels. Further, to determine the impact of
subjective human annotations, plumage condition predictions were compared to manual assessments of one
observer and to matching annotations of two observers. Among all tested settings, performance metrics based on
matching manual annotations of two observers were equal or better than the ones based on annotations of a
single observer. The best result obtained among all tested configurations was a mean average precision (mAP) of
98.02% for hen detection while 91.83% of the plumage condition scores were predicted correctly. Moreover, it
was revealed that performance of hen detection and plumage condition assessment of ChickenNet was not
generally enhanced by depth information. Increasing image resolutions improved plumage assessment up to a
resolution of 896 × 896 pixels, while high detection accuracies (mAP > 0.96) could already be achieved using
lower resolutions. The results indicate that ChickenNet provides a sufficient basis for automated monitoring of
plumage conditions in commercial laying hen farms. keywords: طیور | ارزیابی پر و بال | بینایی کامپیوتر | یادگیری عمیق | تقسیم بندی نمونه | Poultry | Plumage assessment | Computer vision | Deep learning | Instance segmentation |
مقاله انگلیسی |
4 |
Monitoring crop phenology with street-level imagery using computer vision
پایش فنولوژی محصول با تصاویر سطح خیابان با استفاده از بینایی ماشین-2022 Street-level imagery holds a significant potential to scale-up in-situ data collection. This is enabled by combining
the use of cheap high-quality cameras with recent advances in deep learning compute solutions to derive relevant
thematic information. We present a framework to collect and extract crop type and phenological information
from street level imagery using computer vision. Monitoring crop phenology is critical to assess gross primary
productivity and crop yield. During the 2018 growing season, high-definition pictures were captured with side-
looking action cameras in the Flevoland province of the Netherlands. Each month from March to October, a fixed
200-km route was surveyed collecting one picture per second resulting in a total of 400,000 geo-tagged pictures.
At 220 specific parcel locations, detailed on the spot crop phenology observations were recorded for 17 crop
types (including bare soil, green manure, and tulips): bare soil, carrots, green manure, grassland, grass seeds,
maize, onion, potato, summer barley, sugar beet, spring cereals, spring wheat, tulips, vegetables, winter barley,
winter cereals and winter wheat. Furthermore, the time span included specific pre-emergence parcel stages, such
as differently cultivated bare soil for spring and summer crops as well as post-harvest cultivation practices, e.g.
green manuring and catch crops. Classification was done using TensorFlow with a well-known image recognition
model, based on transfer learning with convolutional neural network (MobileNet). A hypertuning methodology
was developed to obtain the best performing model among 160 models. This best model was applied on an
independent inference set discriminating crop type with a Macro F1 score of 88.1% and main phenological stage
at 86.9% at the parcel level. Potential and caveats of the approach along with practical considerations for
implementation and improvement are discussed. The proposed framework speeds up high quality in-situ data
collection and suggests avenues for massive data collection via automated classification using computer vision. keywords: Phenology | Plant recognition | Agriculture | Computer vision | Deep learning | Remote sensing | CNN | BBCH | Crop type | Street view imagery | Survey | In-situ | Earth observation | Parcel | In situ |
مقاله انگلیسی |
5 |
Computer vision-based illumination-robust and multi-point simultaneous structural displacement measuring method
روش اندازه گیری جابجایی ساختاری همزمان با روشنایی مبتنی بر بینایی کامپیوتری-2022 Computer vision-based techniques for structural displacement measurement are rapidly becoming
popular in civil structural engineering. However, most existing computer vision-based displace-
ment measurement methods require man-made targets for object matching or tracking, besides
usually the measurement accuracies are seriously sensitive to the ambient illumination variations.
A computer vision-based illumination robust and multi-point simultaneous measuring method is
proposed for structural displacement measurements. The method consists of two part, one is for
segmenting the beam body from its background, the segmentation is perfectly carried out by fully
convolutional network (FCN) and conditional random field (CRF); another is digital image cor-
relation (DIC)-based displacement measurement. A simply supported beam is built in laboratory.
The accuracy and illumination robustness are verified through three groups of elaborately
designed experiments. Due to the exploitation of FCN and CRF for pixel-wise segmentation,
numbers of locations along with the segmented beam body can be chosen and measured simul-
taneously. It is verified that the method is illumination robust since the displacement measure-
ments are with the smallest fluctuations to the illumination variations. The proposed method does
not require any man-made targets attached on the structure, but because of the exploitation of
DIC in displacement measurement, the regions centered on the measuring points need to have
texture feature. keywords: پایش سلامت سازه | اندازه گیری جابجایی | بینایی کامپیوتر | یادگیری عمیق | تقسیم بندی شی | همبستگی تصویر دیجیتال | Structural health monitoring | Displacement measurement | Computer vision | Deep learning | Object segmentation | Digital image correlation |
مقاله انگلیسی |
6 |
Computer vision-based classification of concrete spall severity using metaheuristic-optimized Extreme Gradient Boosting Machine and Deep Convolutional Neural Network
طبقه بندی مبتنی بر بینایی کامپیوتری شدت پاشش بتن با استفاده از ماشین تقویت کننده گرادیان قویا بهینه شده فراابتکاری و شبکه عصبی پیچیده عمیق-2022 This paper presents alternative solutions for classifying concrete spall severity based on computer vision ap-
proaches. Extreme Gradient Boosting Machine (XGBoost) and Deep Convolutional Neural Network (DCNN) are
employed for categorizing image samples into two classes: shallow spall and deep spall. To delineate the
properties of a concrete surface subject to spall, texture descriptors including local binary pattern, center sym-
metric local binary pattern, local ternary pattern, and attractive repulsive center symmetric local binary pattern
(ARCS-LBP) are employed as feature extraction methods. In addition, the prediction performance of XGBoost is
enhanced by Aquila optimizer metaheuristic. Meanwhile, DCNN is capable of performing image classification
directly without the need for texture descriptors. Experimental results with a dataset containing real-world
concrete surface images and 20 independent model evaluations point out that the XGBoost optimized by the
Aquila metaheuristic and used with ARCS-LBP has achieved an outstanding classification performance with a
classification accuracy rate of roughly 99%. keywords: شدت ریزش بتن | دستگاه افزایش گرادیان | الگوی باینری محلی | فراماسونری | یادگیری عمیق | Concrete spall severity | Gradient boosting machine | Local binary pattern | Metaheuristic | Deep learning |
مقاله انگلیسی |
7 |
Detection of loosening angle for mark bolted joints with computer vision and geometric imaging
تشخیص زاویه شل شدن اتصالات پیچ شده با بینایی ماشین و تصویربرداری هندسی-2022 Mark bars drawn on the surfaces of bolted joints are widely used to indicate the severity of loosening. The
automatic and accurate determination of the loosening angle of mark bolted joints is a challenging issue that has
not been investigated previously. This determination will release workers from heavy workloads. This study
proposes an automated method for detecting the loosening angle of mark bolted joints by integrating computer
vision and geometric imaging theory. This novel method contained three integrated modules. The first module
used a Keypoint Regional Convolutional Neural Network (Keypoint-RCNN)-based deep learning algorithm to
detect five keypoints and locate the region of interest (RoI). The second module recognised the mark ellipse and
mark points using the transformation of the five detected keypoints and several image processing technologies
such as dilation and expansion algorithms, a skeleton algorithm, and the least square method. In the last module,
according to the geometric imaging theory, we derived a precise expression to calculate the loosening angle using
the information for the mark points and mark ellipse. In lab-scale and real-scale environments, the average
relative detection error was only 3.5%. This indicated that our method could accurately calculate the loosening
angles of marked bolted joints even when the images were captured from an arbitrary view. In the future, some
segmentation algorithms based on deep learning, distortion correction, accurate angle and length measuring
instruments, and advanced transformation methods can be applied to further improve detection accuracy. keywords: Mark bolted joint | Loosening detection | Keypoint-RCNN | Image processing | Geometric imaging |
مقاله انگلیسی |
8 |
Survey on deep learning based computer vision for sonar imagery
مروری بر بینایی کامپیوتری مبتنی بر یادگیری عمیق برای تصاویر سونار-2022 Research on the automatic analysis of sonar images has focused on classical, i.e. non deep learning based,
approaches for a long time. Over the past 15 years, however, the application of deep learning in this research
field has constantly grown. This paper gives a broad overview of past and current research involving deep
learning for feature extraction, classification, detection and segmentation of sidescan and synthetic aperture
sonar imagery. Most research in this field has been directed towards the investigation of convolutional neural
networks (CNN) for feature extraction and classification tasks, with the result that even small CNNs with up
to four layers outperform conventional methods.
The purpose of this work is twofold. On one hand, due to the quick development of deep learning it serves as an introduction for researchers, either just starting their work in this specific field or working on classical methods for the past years, and helps them to learn about the recent achievements. On the other hand, our main goal is to guide further research in this field by identifying main research gaps to bridge. We propose to leverage the research in this field by combining available data into an open source dataset as well as carrying out comparative studies on developed deep learning methods. keywords: یادگیری عمیق | تصویربرداری سوناری | کامپیوتری | تشخیص خودکار هدف | Statusquoreview | Deeplearning | Sonarimagery | Computervision | Automatictargetrecognition | Statusquoreview |
مقاله انگلیسی |
9 |
A combined real-time intelligent fire detection and forecasting approach through cameras based on computer vision method
یک رویکرد تشخیص و پیشبینی حریق هوشمند ترکیبی در زمان واقعی از طریق دوربینها بر اساس روش بینایی کامپیوتری-2022 Fire is one of the most common hazards in the process industry. Until today, most fire alarms have had very
limited functionality. Normally, only a simple alarm is triggered without any specific information about the fire
circumstances provided, not to mention fire forecasting. In this paper, a combined real-time intelligent fire
detection and forecasting approach through cameras is discussed with extracting and predicting fire development
characteristics. Three parameters (fire spread position, fire spread speed and flame width) are used to charac-
terize the fire development. Two neural networks are established, i.e., the Region-Convolutional Neural Network
(RCNN) for fire characteristic extraction through fire detection and the Residual Network (ResNet) for fire
forecasting. By designing 12 sets of cable fire experiments with different fire developing conditions, the accu-
racies of fire parameters extraction and forecasting are evaluated. Results show that the mean relative error
(MRE) of extraction by RCNN for the three parameters are around 4–13%, 6–20% and 11–37%, respectively.
Meanwhile, the MRE of forecasting by ResNet for the three parameters are around 4–13%, 11–33% and 12–48%,
respectively. It confirms that the proposed approach can provide a feasible solution for quantifying fire devel-
opment and improve industrial fire safety, e.g., forecasting the fire development trends, assessing the severity of
accidents, estimating the accident losses in real time and guiding the fire fighting and rescue tactics. keywords: ایمنی آتش سوزی صنعتی | تشخیص حریق | پیش بینی آتش سوزی | تجزیه و تحلیل آتش سوزی | هوش مصنوعی | Industrial fire safety | Fire detection | Fire forecasting | Fire analysis | Artificial intelligence |
مقاله انگلیسی |
10 |
A computer vision framework using Convolutional Neural Networks for airport-airside surveillance
چارچوب بینایی کامپیوتری با استفاده از شبکههای عصبی کانولوشن برای نظارت در فرودگاه-2022 Modern airports often have large and complex airside environments featuring multiple runways, with changing configurations, numerous taxiways for effective circulation of flights and tens, if not hundreds, of gates. With inherent uncertainties in gate push-back and taxiway routing, efficient surveillance and management of airport-airside operations is a highly challenging task for air traffic controllers. An increase in air traffic may lead to gate delays, taxiway congestion, taxiway incursions as well as significant increase in the workload of air traffic controllers. With the advent of Digital Towers, airports are increasingly being equipped with surveillance camera systems. This paper proposes a novel computer vision framework for airport-airside surveillance, using cameras to monitor ground movement objects for safety enhancement and operational efficiency improvement. The framework adopts Convolutional Neural Networks and camera calibration techniques for aircraft detection and tracking, push-back prediction, and maneuvering monitoring. The proposed framework is applied on video camera feeds from Houston Airport, USA (for maneuvering monitoring) and Obihiro Airport, Japan (for push-back prediction). The object detection models of the proposed framework achieve up to 73.36% average precision on Houston airport and 87.3% on Obihiro airport. The framework estimates aircraft speed and distance with low error (up to 6 meters), and aircraft push-back is predicted with an average error of 3 min from the time an aircraft arrives with the error-rate reducing until the aircraft’s actual push-back event.
keywords: Air traffic control | Convolutional Neural Network | Computer vision |
مقاله انگلیسی |