Object Detection Based on YOLO Network

Liu, Chengji; Tao, Yun; Liang, Jiawei; Li, Kai; Chen, Yihang

doi:10.1109/itoec.2018.8740604

Cited by 143 publications

(52 citation statements)

References 13 publications

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“…Hence these people require an assistance by which they can visualize the outside world and live a normal life. Various research papers like "Wearable object detection system for the blind" by Alessandro Dionisi, Emilio Sardini, Mauro Serpelloni [11] and "A mobility aid for the blind with discrete distance indicator and hanging object detection" [2] explained the need to design a system which will provide an assistance to the visually impaired people to navigate freely from the source to destination with no or minimal help. Blind people are always dependent on others to cross road or to reach a particular address.…”

Section: Literature Surveymentioning

confidence: 99%

“…Therefore, we build a system called EOA that will help them to carry out their activities like normal people to some extent. Various algorithms and measurements were studied from different papers like "Object Detection Based on YOLO Network" [2], "Real Time Object Detection, Tracking, and Distance and Motion Estimation based on Deep Learning: Application to Smart Mobility" [19] etc. that encouraged us to select the correct algorithm to carry out our work.…”

Section: Literature Surveymentioning

confidence: 99%

“…• YOLO algorithm which passes the image through the complexities of a neural network architecture called Darknet [2] • OpenCV is the computer vision library/ framework that we will be using to support our YOLOv3 algorithm. OpenCV has inbuilt support for Darknet Architecture [14].…”

Section: Yolo Algorithm For Object Detectionmentioning

confidence: 99%

See 2 more Smart Citations

Electronic Orientation Aid for Visually Impaired using Graphics Processing Unit (GPU)

et al. 2020

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Vision is one of the very essential human senses and it plays a major role in human perception about surrounding environment. But for people with visual impairment their definition of vision is different. Visually impaired people are often unaware of dangers in front of them, even in familiar environment. This study proposes a real time guiding system for visually impaired people for solving their navigation problem and to travel without any difficulty. This system will help the visually impaired people by detecting the objects and giving necessary information about that object. This information may include what the object is, its location, its precision, distance from the visually impaired etc. All these information will be conveyed to the person through audio commands so that they can navigate freely anywhere anytime with no or minimal assistance. Object detection is done using You Only Look Once (YOLO) algorithm. As the process of capturing the video/images and sending it to the main module has to be carried at greater speed, Graphics Processing Unit (GPU) is used. This will help in enhancing the overall speed of the system and will help the visually Impaired to get the maximum necessary instructions as quickly as possible. The process starts from capturing the real time video, sending it for analysis and processing and get the calculated results. The results obtained from analysis are conveyed to user by means of hearing aid. As a result by this system the blind or the visually impaired people can visualize the surrounding environment and travel freely from source to destination on their own.

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

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Electronic Orientation Aid for Visually Impaired using Graphics Processing Unit (GPU)

et al. 2020

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“…Where B is Bounding Box and C is class probabilities [3]. YOLO network is designed based on the backbone of Google Net [4].…”

Section: Yolo (You Only Look Once)mentioning

confidence: 99%

Smart Classroom using Object Detection

Patel¹

2020

IJRASET

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We have transitioned in an era from traditional teaching equipment's using (Blackboards, chalk) to technology equipped (projector, computer, surveillance cameras) classroom known to us as smart classrooms. But are they really Smart Classrooms? These equipment's are powered by electricity and it's important to switch off the appliances after use, but we tend to forget due to laziness or dependency on peon for this task, this causes a very huge wastage of electricity. Thus, our project aims to solve this problem once and for all by automatic switching on & off the appliances of the classrooms when there is no one in the classroom. We aim to achieve this solution using a mixture of Machine Learning (Image processing, Convolutional Neural Network) for detection of human presence in the classroom, then IOT (Raspberry pi, Relay, Generic Camera) to switch the appliances on or off.

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“…Since the study of object detection has been grown rapidly and the computer technology is sufficient for heavy computation, Redmon, et.al [24] introduced the new object detection method called You Only Look Once (YOLO) which the structure consists of some convolutional layer and this method is able to predict the bounding box image and object coordinate precisely. This method is the open source, thus, make some researchers are able to implemented this method on their study i.e., for robotics in detecting ball and goals [25], improved the traffic signs [26], detected human face [27,28], detected pedestrian [29], and hand gesture detection [30]. Two years later, Redmon, et.al, introduced the YOLO V3 which has the architecture bigger than last version but three times faster in detecting and has the accuracy the same as SSD algorithm [31].…”

Section: Introductionmentioning

confidence: 99%

Industry 4.0: Hand Recognition on Assembly Supervision Process

Analia

Pratama

Susanto

2021

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In the assembly industry, the process of assembling components is very important in order to produce a quality product. Assembly of components should be carried out sequentially based on the standards set by the company. For companies that still operate the assembly process manually by employee, sometimes errors occur in the assembly process, which can affect the quality of production. In order to be carried out the assembly process according to the procedure, a system is needed that can detect employee hands when carrying out the assembly process automatically. This study proposes an artificial intelligence-based real-time employee hand detection system. This system will be the basis for the development of an automatic industrial product assembly process to welcome the Industry 4.0. To verify system performance, several experiments were carried out, such as; detecting the right and left hands of employees and detecting hands when using accessories or not. From the experimental results it can be concluded that the system is able to detect the right and left hands of employees well with the resulting FPS average of 15.4.

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Object Detection Based on YOLO Network

Cited by 143 publications

References 13 publications

Electronic Orientation Aid for Visually Impaired using Graphics Processing Unit (GPU)

Electronic Orientation Aid for Visually Impaired using Graphics Processing Unit (GPU)

Smart Classroom using Object Detection

Industry 4.0: Hand Recognition on Assembly Supervision Process

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