دانلود و نمایش مقالات مرتبط با Object detection::صفحه 2
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نتیجه جستجو - Object detection

تعداد مقالات یافته شده: 50
ردیف عنوان نوع
11 Taxonomy, state-of-the-art, challenges and applications of visual understanding: A review
طبقه بندی ، آخرین فن آوری ، چالش ها و کاربردهای درک بصری: مروری-2021
Since the dawn of Humanity, to communicate both abstract and concrete ideas, visualization through visual imagery has been an effective way. With the advancement of scientific technologies, vision has been imparted to machines like humans do. Computer vision give ability to machines, to receive and analyze visual data on its own, and then make decisions about it, hence computer vision is more than machine learning applied. So, visualization of computer models to learn without being explicitly programmed using machine learning algorithms is called Visual learning. This work aims to review the state-of-the-art in computer vision by highlighting the contributions, challenges and applications. We first provide an overview of important visual learning approaches and their recent developments, and then describes their applications in diverse vision tasks, such as image classification, object detection, object recognition, visual saliency detection, semantic and instance segmentation, human pose estimation and image retrieval. Hardware constraints are also highlighted for better understanding of model selection. Finally, some important challenges, trends and outlooks are also discussed for better design and training of learning modules, along with several directions that may be further explored in the future.© 2021 Elsevier Inc. All rights reserved.
Keywords: Computer vision | Visual learning | Developments | Applications | Trends | Challenges
مقاله انگلیسی
12 An intelligent and cost-effective remote underwater video device for fish size monitoring
یک دستگاه ویدیویی زیر آب از راه دور هوشمند و مقرون به صرفه برای نظارت بر اندازه ماهی-2021
Monitoring the size of key indicator species of fish is important to understand ecosystem functions, anthropo- genic stress, and population dynamics. Standard methodologies gather data using underwater cameras, but are biased due to the use of baits, limited deployment time, and short field of view. Furthermore, they require experts to analyse long videos to search for species of interest, which is time consuming and expensive. This paper describes the Underwater Detector of Moving Object Size (UDMOS), a cost-effective computer vision system that records events of large fishes passing in front of a camera, using minimalistic hardware and power consumption. UDMOS can be deployed underwater, as an unbaited system, and is also offered as a free-to-use Web Service for batch video-processing. It embeds three different alternative large-object detection algorithms based on deep learning, unsupervised modelling, and motion detection, and can work both in shallow and deep waters with infrared or visible light.
Keywords: Computer vision | Biodiversity conservation | Fish size | Baited remote underwater video | Artificial intelligence | Deep learning | Unsupervised modelling | Motion detection
مقاله انگلیسی
13 A Guide to Annotation of Neurosurgical Intraoperative Video for Machine Learning Analysis and Computer Vision
راهنمای حاشیه نویسی ویدئوی حین عمل جراحی مغز و اعصاب برای تجزیه و تحلیل یادگیری ماشین و بینایی ماشین-2021
- OBJECTIVE: Computer vision (CV) is a subset of artificial intelligence that performs computations on image or video data, permitting the quantitative analysis of visual information. Common CV tasks that may be relevant to surgeons include image classification, object detection and tracking, and extraction of higher order features. Despite the potential applications of CV to intraoperative video, however, few surgeons describe the use of CV. A primary roadblock in implementing CV is the lack of a clear workflow to create an intraoperative video dataset to which CV can be applied. We report general principles for creating usable surgical video datasets and the result of their applications.
- METHODS: Video annotations from cadaveric endoscopic endonasal skull base simulations (n [ 20 trials of 1e5 minutes, size [ 8 GB) were reviewed by 2 researcher annotators. An internal, retrospective analysis of workflow for development of the intraoperative video annotations was performed to identify guiding practices.
- RESULTS: Approximately 34,000 frames of surgical video were annotated. Key considerations in developing annotation workflows include 1) overcoming software and personnel constraints; 2) ensuring adequate storage and access infrastructure; 3) optimization and standardization of annotation protocol; and 4) operationalizing annotated data. Potential tools for use include CVAT (Computer Vision Annotation Tool) and Vott: open-sourced annotation software allowing for local video storage, easy setup, and the use of interpolation.
- CONCLUSIONS: CV techniques can be applied to surgical video, but challenges for novice users may limit adoption. We outline principles in annotation workflow that can mitigate initial challenges groups may have when converting raw video into useable, annotated datasets.
Key words: Artificial intelligence | Computer vision | Intraoperative video | Machine learning
مقاله انگلیسی
14 Color Image Enhancement based on Gamma Encoding and Histogram Equalization
بهبود تصویر رنگی بر اساس رمزگذاری گاما و یکسان سازی هیستوگرام-2021
Image Enhancement is used as a preprocessing step in many computer vision applications. It provides enhanced input for other computerized image processing methods. Many preprocessing techniques can be applied to images depending on the application domain. In this paper we are proposing an image enhancement technique for color images that can be used as preprocessing step in many computer vision applications. It can also be used as a data augmentation technique in object detection. Luminance component of images is sometimes not captured by cameras and displayed by monitors properly. To remove this drawback of devices we have used gamma encoding. Four different values of gamma are evaluated depending on the quality of images. Image is then converted into YUV Color space. Y component represents the luminance. U and V components represent color. After that Contrast Limited Adaptive Histogram Equalization is applied to the Y component to improve the contrast of the image. The results are compared with the state-of-the-art methods on the basis of Peak Signal to noise Ratio (PSNR) and Mean Square Error (MSE). Quantitative results show that proposed algorithm results in improved value of PSNR and decreased value of MSE as compared to existing methods. Qualitative comparison is also done and results show improvement over the existing techniques.© 2020 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Materials, Manufacturing and Mechanical Engineering for Sustainable Developments-2020.
Keywords: Histogram | Intensity | Luminance | Contrast stretching
مقاله انگلیسی
15 Object detection and position tracking in real time using Raspberry Pi
تشخیص و ردیابی موقعیت در زمان واقعی با استفاده از رزبری پای-2021
One of the fast-growing areas of deep learning using artificial intelligence is computer vision, becoming increasingly popular. It is a growing field of research that seeks to create techniques to help computers ‘‘see” and recognize the information of digital images for instance videos and photographs. Object detection is a computer vision method that enables us to recognize objects in an image or video and locate them. This article describes an efficient shape-based object identification method and its displacement in real-time using OpenCV library of programming roles mostly targeted at computer vision and Raspberry Pi with camera module.© 2021 Elsevier Ltd. All rights reserved.
Keywords: OpenCV | Position | Object detection | Displacement | Automation
مقاله انگلیسی
16 Deep learning-based real-world object detection and improved anomaly detection for surveillance videos
تشخیص واقعی شیء مبتنی بر یادگیری عمیق و بهبود تشخیص ناهنجاری برای فیلم های نظارتی-2021
In this fast processing world, we need fast processing programs with maximum accuracy. This can be achieved when computer vision is connected with optimized deep learning models and neural networks. The goal of this project is to build an Artificial Intelligent system that will take live CCTV camera feed as input and detect what is happening in the video and do further analysis. Concerning current technology, there are a lot many models which use computer vision, machine learning for image and video processing. All models are different from each other, use various libraries, and are difficult to integrate or need high-end systems to process. This paper aims to use a convolutional neural network model for video processing and solve most of the important video processing features like detection of the liveliness of objects, estimating counts, and anomaly detection. And also further deploy it in such a way that it’ll be easy to integrate and easy to use with API calls.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Computer vision | Convolutional neural network | Deep learning | Estimating counts | Liveliness
مقاله انگلیسی
17 Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues
یادگیری عمیق برای تشخیص شی و درک صحنه در اتومبیل های خودران: بررسی ، چالش ها و مسائل باز-2021
This article presents a comprehensive survey of deep learning applications for object detection and scene perception in autonomous vehicles. Unlike existing review papers, we examine the theory underlying self-driving vehicles from deep learning perspective and current implementations, followed by their critical evaluations. Deep learning is one potential solution for object detection and scene perception problems, which can enable algorithm driven and data-driven cars. In this article, we aim to bridge the gap between deep learning and self-driving cars through a comprehensive survey. We begin with an introduction to self-driving cars, deep learning, and computer vision followed by an overview of artificial general intelligence. Then, we classify existing powerful deep learning libraries and their role and significance in the growth of deep learning. Finally, we discuss several techniques that address the image perception issues in real-time driving, and critically evaluate recent implementations and tests conducted on self-driving cars. The findings and practices at various stages are summarized to correlate prevalent and futuristic techniques, and the applicability, scalability and feasibility of deep learning to self-driving cars for achieving safe driving without human intervention. Based on the current survey, several recommendations for further research are discussed at the end of this article.
Keywords: Self-driving cars | Levels of automation | Machine learning | Deep learning | Convolutional neural networks | Scene perception | Object detection | Multimodal sensor fusion | LiDAR | Computer vision | Autonomous driving initiatives
مقاله انگلیسی
18 Smart Technologies for Visually Impaired: Assisting and conquering infirmity of blind people using AI Technologies
فناوری های هوشمند برای افراد دارای اختلال بینایی : کمک و تسخیر ناتوانی افراد نابینا با استفاده از فناوری های هوشمند مصنوعی-2020
Physical disability has affected many people’s lives across the world. One of these disabilities that strongly affected some large category of people is visual lose. Blind people often face difficulties in moving around freely such as: in crossing the street, in reading, driving or socializing. They often rely on using certain aid devices to reach certain places or perform any other daily activities such as walking sticks. There are ongoing scientific researches in the area of rectifying blindness, but it has to go long way to achieve the solution. Also, there are research unleashes the ideas of assisting the blind people deficiency but lacks in technological aspects of implementation. This research project aims at helping blind people of all categories to achieve their day to day tasks easier through the use of a smart device. By using artificial intelligent and image processing, this smart device is able to detect faces, colors and deferent objects. The detection process is manifested by notifying the visually impaired person through either a sound alert or vibration. Additionally, this study presents a palpable survey that entails visually impaired people from the local community. Subsequently, the project uses both Open CV and Python for programming and implementation. The exertion of this project prototype investigates the algorithms which are used for detecting the objects. Also, it demonstrates how this smart device could detects certain physical object and how it could send a warning signal when faced by any obstacles. Overall, this research will be a positive addition in the world of health care sector by supporting blind people with the use of smart technology.
Keywords: Artificial Intelligent | Open CV | Python | Face Recognition | Object Detection | Health Care Introduction
مقاله انگلیسی
19 Fine-grained vehicle type detection and recognition based on dense attention network
ردیابی و تشخیص نوع وسیله نقلیه ریز براساس شبکه توجه متراکم-2020
Intelligent transportation is an indispensable part of the smart city and the primary development direc- tion of the future transportation systems. Vehicle detection and recognition, which is one of the most im- portant aspects of intelligent transportation, plays a very important role in various areas of our daily life; one such important area is criminal investigation. In the fine-grained vehicle type detection and recog- nition, several difficult issues such as problems in data acquisition and tagging, dramatic variance in the data of different vehicle types, and challenges in identifying vehicles of the same brand with highly sim- ilar appearances remain unsolved. For the problems of data acquisition and tagging, this paper presents a strategy for automatic data acquisition and tagging based on object detection that can label the vehicle images efficiently while rapidly acquiring all types of fine-grained models. Considering the problem of data imbalance in the training process, this paper proposes a Faster-RCNN based data equalizing strategy (Faster-BRCNN), thereby improving the performance of object detection. In view of the severe informa- tion attenuation caused by the feature information transfer obstruct between layers in the traditional deep learning network, the lack of mutual dependency of these features, and the inability of the network to focus on the important region and characteristics, we propose an intensive dense attention network (DA-Net). Through its intensive connection and attention unit, we enhance the model’s detection abil- ity. The proposed method achieves mAP of 94.5% and 95.8% in the Stanford Cars and FZU Cars datasets, respectively, thereby verifying its effectiveness.
مقاله انگلیسی
20 Hiding Private Information in Images From AI
پنهان کردن اطلاعات خصوصی در تصاویر از هوش مصنوعی-2020
Privacy protection attracts increasing concerns these days. People tend to believe that large social platforms will comply with the agreement to protect their privacy. However, photos uploaded by people are usually not treated to achieve privacy protection. For example, Facebook, the world’s largest social platform, was found leaking photos of millions of users to commercial organizations for big data analytics. A common analytical tool used by these commercial organizations is the Deep Neural Network (DNN). Today’s DNN can accurately identify people’s appearance, body shape, hobbies and even more sensitive personal information, such as addresses, phone numbers, emails, bank cards and so on. To enable people to enjoy sharing photos without worrying about their privacy, we propose an algorithm that allows users to selectively protect their privacy while preserving the contextual information contained in images. The results show that the proposed algorithm can select and perturb private objects to be protected among multiple optional objects so that the DNN can only identify non-private objects in images.
Index Terms: privacy | object detection | deep learning
مقاله انگلیسی
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