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1 |
Computer vision for solid waste sorting: A critical review of academic research
بینایی کامپیوتری برای تفکیک زباله جامد: مروری انتقادی تحقیقات دانشگاهی-2022 Waste sorting is highly recommended for municipal solid waste (MSW) management. Increasingly, computer
vision (CV), robotics, and other smart technologies are used for MSW sorting. Particularly, the field of CV-
enabled waste sorting is experiencing an unprecedented explosion of academic research. However, little atten-
tion has been paid to understanding its evolvement path, status quo, and prospects and challenges ahead. To
address the knowledge gap, this paper provides a critical review of academic research that focuses on CV-enabled
MSW sorting. Prevalent CV algorithms, in particular their technical rationales and prediction performance, are
introduced and compared. The distribution of academic research outputs is also examined from the aspects of
waste sources, task objectives, application domains, and dataset accessibility. The review discovers a trend of
shifting from traditional machine learning to deep learning algorithms. The robustness of CV for waste sorting is
increasingly enhanced owing to the improved computation powers and algorithms. Academic studies were un-
evenly distributed in different sectors such as household, commerce and institution, and construction. Too often,
researchers reported some preliminary studies using simplified environments and artificially collected data.
Future research efforts are encouraged to consider the complexities of real-world scenarios and implement CV in
industrial waste sorting practice. This paper also calls for open sharing of waste image datasets for interested
researchers to train and evaluate their CV algorithms. keywords: زباله جامد شهری | تفکیک زباله | بینایی ماشین | تشخیص تصویر | یادگیری ماشین | یادگیری عمیق | Municipal solid waste | Waste sorting | Computer vision | Image recognition | Machine learning | Deep learning |
مقاله انگلیسی |
2 |
Evaluation of corporate requirements for smart manufacturing systems using predictive analytics
ارزیابی الزامات شرکت برای سیستمهای تولید هوشمند با استفاده از تجزیه و تحلیل پیشبینیکننده-2022 Smart manufacturing systems (SMS) are one of the most important applications in the Industry
4.0 era, offering numerous advantages over traditional production systems and rapidly being
used as a performance-enhancing strategy of manufacturing enterprises. A few of the technologies that must be connected to construct an SMS are the Industrial Internet of Things (IIoT),
Big Data, Robotics, Blockchain, 5G Communication, Artificial Intelligence (AI), and many more.
SMS is an innovative and popular manufacturing setup that produces increasingly intelligent
production systems; yet, designers must adapt to business tastes and requirements. This study
employs an analytical and descriptive research technique to identify and assess functional and
non-functional, technological, economic, social, and performance evaluation components that
are essential to SMS evaluation. A predictive analytics framework, which is a key component
of many decision support systems, is used to assess corporate needs as well as proposed and
prioritize SMS services.
keywords: صنعت 4.0 | تجزیه و تحلیل پیش بینی کننده | سیستم های تولید هوشمند | اینترنت اشیاء صنعتی | سیستم پشتیبانی تصمیم | Industry4.0 | Predictive analytics | Smart manufacturing systems | Industrial Internet of Things | Decision support system |
مقاله انگلیسی |
3 |
Compensating over- and underexposure in optical target pose determination
Compensating over- and underexposure in optical target pose determination-2021 Optical coded targets allow to determine the relative pose of a camera, on a metric scale, from one image only. Furthermore, they are easily and efficiently detected, opening to a wide range of applications in robotics and computer vision. In this work we describe the effect of pixel saturation and non-ideal lens Point Spread Function, causing the apparent position of the corners and the edges of the target to change as a function of the camera exposure time. This effect, which we call exposure bias, is frequent in over- or underexposed images and introduces a systematic error in the estimated camera pose. We propose an algorithm that is able to estimate and correct for the exposure bias exploiting specific geometric features of a common target design based on concentric circles. Through rigorous laboratory experiments carried out in a highly controlled environment, we demonstrate that the proposed algorithm is seven times more precise and three times more accurate in the target distance estimation than the algorithms available in the literature.© 2021 Elsevier Ltd. All rights reserved. Keywords: Optical target | Target orientation | Image processing algorithm | Geometry | Ellipse fitting | Computer vision | Overexposure | Exposure compensation | Resection |
مقاله انگلیسی |
4 |
Real-time plant phenomics under robotic farming setup: A vision-based platform for complex plant phenotyping tasks
پدیده های گیاهی در زمان واقعی تحت راه اندازی رباتیک کشاورزی: یک پلت فرم مبتنی بر دید برای کارهای پیچیده فنوتیپ سازی گیاهان-2021 Plant phenotyping in general refers to quantitative estimation of the plant’s anatomical, ontogenetical, physiological and biochemical properties. Analyzing big data is challenging, and non-trivial given the different complexities involved. Efficient processing and analysis pipelines are the need of the hour with the increasing popularity of phenotyping technologies and sensors. Through this work, we largely address the overlapping object segmentation & localization problem. Further, we dwell upon multi-plant pipelines that pose challenges as detection and multi-object tracking becomes critical for single frame/set of frames aimed towards uniform tagging & visual features extraction. A plant phenotyping tool named RTPP (Real-Time Plant Phenotyping) is presented that can aid in the detection of single/multi plant traits, modeling, and visualization for agricultural settings. We compare our system with the plantCV platform. The relationship of the digital estimations, and the measured plant traits are discussed that plays a vital roadmap towards precision farming and/or plant breeding. Keywords: Phenotype | Image processing | Spectral | Robotics | Object localization | Precision agriculture | Plant science | Pattern recognition | Computer vision | Automation | Perception |
مقاله انگلیسی |
5 |
Weighted boxes fusion: Ensembling boxes from different object detection models
همجوشی جعبه های توزین شده: جمع آوری جعبه هایی از مدل های مختلف تشخیص شیء-2021 Object detection is a crucial task in computer vision systems with a wide range of applications in autonomous driving, medical imaging, retail, security, face recognition, robotics, and others. Nowadays, neural networks- based models are used to localize and classify instances of objects of particular classes. When real-time inference is not required, ensembles of models help to achieve better results. In this work, we present a novel method for fusing predictions from different object detection models: weighted boxes fusion. Our algorithm utilizes confidence scores of all proposed bounding boxes to construct averaged boxes. We tested the method on several datasets and evaluated it in the context of Open Images and COCO Object Detection challenges, achieving top results in these challenges. The 3D version of boxes fusion was successfully applied by the winning teams of Waymo Open Dataset and Lyft 3D Object Detection for Autonomous Vehicles challenges. The source code is publicly available at GitHub (Solovyev, 2019 [31]).We present a novel method for combining predictions in ensembles of different object detection models: weighted boxes fusion. This method significantly improves the quality of the fused predicted rectangles for an ensemble. We tested the method on several datasets and evaluated it in the context of the Open Images and COCO Object Detection challenges. It helped to achieve top results in these challenges. The source code is publicly available at GitHub.© 2021 Published by Elsevier B.V. Keywords: Object detection | Computer vision | Deep learning |
مقاله انگلیسی |
6 |
A review of multimodal human activity recognition with special emphasis on classification, applications, challenges and future directions
مروری بر به رسمیت شناختن فعالیتهای چندمنظوره انسان با تأکید ویژه بر طبقه بندی ، کاربردها ، چالشها و جهت های آینده-2021 Human activity recognition (HAR) is one of the most important and challenging problems in the computer vision. It has critical application in wide variety of tasks including gaming, human– robot interaction, rehabilitation, sports, health monitoring, video surveillance, and robotics. HAR is challenging due to the complex posture made by the human and multiple people interaction. Various artifacts that commonly appears in the scene such as illuminations variations, clutter, occlusions, background diversity further adds the complexity to HAR. Sensors for multiple modalities could be used to overcome some of these inherent challenges. Such sensors could include an RGB-D camera, infrared sensors, thermal cameras, inertial sensors, etc. This article introduces a comprehensive review of different multimodal human activity recognition methods where different types of sensors being used along with their analytical approaches and fusion methods. Further, this article presents classification and discussion of existing work within seven rational aspects: (a) what are the applications of HAR; (b) what are the single and multi-modality sensing for HAR; (c) what are different vision based approaches for HAR; (d) what and how wearable sensors based system contributes to the HAR; (e) what are different multimodal HAR methods; (f) how a combination of vision and wearable inertial sensors based system contributes to the HAR; and (g) challenges and future directions in HAR. With a more and comprehensive understanding of multimodal human activity recognition, more research in this direction can be motivated and refined.© 2021 Elsevier B.V. All rights reserved. Keywords: Activity recognition | Computer vision | Wearable sensors | Fusion of vision and inertial sensors | Smart-shoes | Multimodality |
مقاله انگلیسی |
7 |
Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions
کاربردهای هوش مصنوعی در زنجیره تأمین: تحلیل کتابشناختی توصیفی و مسیرهای تحقیق در آینده-2021 Today’s supply chains are very different from those of just a few years ago, and they continue to evolve within an extremely competitive economy. Dynamic supply chain processes require a technology that can cope with their increasing complexity. In recent years, several functional supply chain applications based on artificial intelligence (AI) have emerged, yet very few studies have addressed the applications of AI in supply chain processes. Machine learning, natural language processing, and robotics are all potential enablers of supply chain trans- formation. Aware of the potential advantages of AI implementation in supply chains and of the paucity of work done regarding it, we explore what researchers have done so far with respect to AI and what needs further exploration. We reviewed 136 research papers published between 1996 and 2020 from the Scopus database and provided a classification of the research material according to four critical structural dimensions (level of analytics, AI algorithms or techniques, sector or industry of application, and supply chain processes). This study is the first attempt to study the AI applications in SC from a process perspective and provides a decisional framework for adequate use of AI techniques in the different SC processes. Keywords: Artificial intelligence | Supply chain | Bibliometric analysis | Systematic literature review | Classification |
مقاله انگلیسی |
8 |
18: Data deduplication applications in cognitive science and computer vision research
18: کاربردهای تکثیر داده ها در علوم شناختی و تحقیقات بینایی ماشین-2021 Advances in technologies in the field of computing and semiconductor research have made the
devices and gadgets portable, which are used in all modern applications ranging from healthcare
to robotics and self-driving car. These applications require capturing huge amount of information
through various sensors in the form of images, videos, and other physical stimuli. If the data are in
the form of images or videos, then the storage becomes a matter of concern. Some image processing is involved where images are processed using suitable signal processing systems and components. The typical computer vision (CV) system that actually mimics human vision system is
shown in Fig. 181. We can see in the figure that some stimuli indicated by some physical excitation is captured by sensors and sent for preprocessing to make the captured data appropriate for
further operation. Once the data become proper for specific application, it is subjected to some
machine learning methods or artificial intelligence for understanding of the data. This may be
used as the process for learning the data and getting some suitable features that would be used in
postprocessing of the data. CV then classifies the data and outputs some decisions or commands
depending on the applications. The data that are used in CV are mainly of image types and the
operation is image processing.
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مقاله انگلیسی |
9 |
Reinforcement learning based on movement primitives for contact tasks
یادگیری تقویتی بر اساس ابتدای حرکت برای وظایف تماس-2020 Recently, robot learning through deep reinforcement learning has incorporated various robot tasks through deep
neural networks, without using specific control or recognition algorithms. However, this learning method is
difficult to apply to the contact tasks of a robot, due to the exertion of excessive force from the random search
process of reinforcement learning. Therefore, when applying reinforcement learning to contact tasks, solving the
contact problem using an existing force controller is necessary. A neural-network-based movement primitive
(NNMP) that generates a continuous trajectory which can be transmitted to the force controller and learned
through a deep deterministic policy gradient (DDPG) algorithm is proposed for this study. In addition, an
imitation learning algorithm suitable for NNMP is proposed such that the trajectories similar to the demonstration
trajectory are stably generated. The performance of the proposed algorithms was verified using a square
peg-in-hole assembly task with a tolerance of 0.1 mm. The results confirm that the complicated assembly trajectory
can be learned stably through NNMP by the proposed imitation learning algorithm, and that the assembly
trajectory is improved by learning the proposed NNMP through the DDPG algorithm. Keywords: AI-based methods | Force control | Deep Learning in robotics and automation |
مقاله انگلیسی |
10 |
Analytical study on use of AI techniques in tourism sector for smarter customer experience management
مطالعه تحلیلی در مورد استفاده از تکنیک های هوش مصنوعی در بخش گردشگری برای مدیریت دقیقتر تجربه مشتری-2020 Artificial Intelligence is the new prime factor for paradigm shift
of the new age technologies. It has created a new realm in every
field- from education to entertainment or from biotechnology to
manufacturing industry. Though tourism is a late runner in this
race, but this sector has also witnessed a huge change with the
magical touch of AI. This sector being one of the highly emerging
sectors, contributing very high GDP , has adapted several
machine learning techniques or data analytics, which has made
tourism model smarter and dynamic. In India , tourism has an
ample scope to grow and Indian tourism sectors are also
adapting several popular AI techniques like deep learning,
Artificial neural network, predictive analytics, robotics or new
technologies like virtual reality or augmented reality. This
technological adaptation has made their services much better,
heled in dynamic pricing, or for smart customer experience
management. This paper has conducted a study on Indian
tourism sectors providing online services and discusses about the
current AI technologies used by them while exploring the pros
and cons faced by them . The paper is alienated in three different
segments- section 1 contains introduction part, section 2
discusses about related works in similar area, third section
deliberates about different AI techniques adapted by Indian
tourism sectors along with their pro and cons. Keywords : ChatBot | Artificial neural network | Machine Learning | Robotics | Predictive Analytics | Recommendation System |
مقاله انگلیسی |