Relation-aware Siamese region proposal network for visual object tracking

Zhu, Jiaming; Zhang, Guopeng; Zhou, Shibin; Li, Kun

doi:10.1007/s11042-021-10574-z

Cited by 10 publications

(1 citation statement)

References 52 publications

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“…Particularly in the wake of the present worldwide pandemic scenario, when certain healthcare protocols must be followed, including the wearing of facial masks and social separation, face recognition has emerged as one of the topics that is garnering a lot of attention as a hot button issue [18]. Currently, different approaches have been devised for detecting facial masks based on deep learning [19] such as the region proposal network (RPN) [20], [21] and the faster region-based convolutional neural networks (R-CNN) network methods [22]- [24]. On the other hand, the detection speed of these methods is relatively slow, and this is especially the case when they are implemented on low-power processing units like the NVDIA Jetson Nano.…”

Section: Introductionmentioning

confidence: 99%

Face mask detection and counting using you only look once algorithm with Jetson Nano and NVDIA giga texel shader extreme

Al-Selwi

Hassan

Ghani

et al. 2023

IJ-AI

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Deep learning and machine learning are becoming more extensively adopted artificial intelligence techniques for machine vision problems in everyday life, giving rise to new capabilities in every sector of technology. It has a wide range of applications, ranging from autonomous driving to medical and health monitoring. For image detection, the best reported approach is the you only look once (YOLO) algorithm, which is the faster and more accurate version of the convolutional neural network (CNN) algorithm. In the healthcare domain, YOLO can be applied for checking the face mask wearing of the people, especially in a public area or before entering any closed space such as a building to avoid the spread of the air-borne disease such as COVID-19. The main challenges are the image datasets, which are unstructured and may grow large, affecting the accuracy and speed of the detection. Secondly is the portability of the detection devices, which are generally dependent on the more portable like NVDIA Jetson Nano or from the existing computer/laptop. Using the low-power NVDIA Jetson Nano system as well as NVDIA giga texel shader extreme (GTX), this paper aims to design and implement real-time face mask wearing detection using the pre-trained dataset as well as the real-time data.

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Section: Introductionmentioning

confidence: 99%

Face mask detection and counting using you only look once algorithm with Jetson Nano and NVDIA giga texel shader extreme

Al-Selwi

Hassan

Ghani

et al. 2023

IJ-AI

View full text Add to dashboard Cite

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Visual Object Tracking Using Machine Learning

Odeh,

Keshta,

Al-Fayoumi

2023

Communications in Computer and Information Science

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Single system for online monitoring and inspection of automated fiber placement with object segmentation by artificial neural networks

et al. 2022

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The reduction of material defects in the automated fiber placement process is one of the significant factors for manufacturing large and complex components more efficiently in the future. However, the monitoring of complex manufacturing processes usually requires complex sensor and computer systems that are often quite sensitive to disturbances and errors. New techniques such as image segmentation with neural networks provide a new approach to this problem and have the potential to solve complex processes faster and more robustly. In this study, a system is presented that performs monitoring, inspection and measurement tasks simultaneously in automated fiber placement processes. The system is based on the SiamMask network which is used for the automatic image processing. The artificial neural network is trained to recognize individual carbon fiber tapes and segment them for additional analysis. For the creation of the testing- and training data, an analytical approach is presented. The post-processing of the object segmentation, which is the primary output of the SiamMask network and the identification of individual tapes, provides accurate measurements which are demonstrated by an example. We show that image segmentation with modern approaches like SiamMask offers great potential to handle highly complex engineering tasks in a faster and more intelligent manner in comparison to conventional methods.

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Relation-aware Siamese region proposal network for visual object tracking

Cited by 10 publications

References 52 publications

Face mask detection and counting using you only look once algorithm with Jetson Nano and NVDIA giga texel shader extreme

Face mask detection and counting using you only look once algorithm with Jetson Nano and NVDIA giga texel shader extreme

Visual Object Tracking Using Machine Learning

Single system for online monitoring and inspection of automated fiber placement with object segmentation by artificial neural networks

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