Faster R-CNN for Detecting Regions in Human-Annotated Micrograph Images

Albinali, Hussah; Alzahrani, Fatimah Abdulraheem

doi:10.1109/widstaif52235.2021.9430211

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Through research and surveys, the field of security is often overlooked for the vast majority of people, with technology and applications investing relatively little in security, and the deployment and maintenance of security systems usually requiring considerable investment [1]. For organizations or individuals with limited budgets, other urgent needs may take precedence and security may be put on the back burner [2]. Some regions or organizations may have a low perception of security risks and perceive the likelihood of a security incident as low, so that security measures are not seen as an urgent need.…”

Section: Introductionmentioning

confidence: 99%

Deploying human body detection technologies in security systems: An in-depth study of the FASTER-GCNN algorithm

Jiang

2024

ACE

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The field of human body detection, a pivotal area in computer vision, merits comprehensive discussion. Remarkable advancements have been achieved in the techniques for human body detection over the past few decades, with significant applications spanning various sectors. This discussion delves into the potential of human detection technology within the realm of security - a field that necessitates efficient and accurate human detection technology to promptly identify potential threats, suspicious behaviors, or unusual activities. Deep learning-based human detection algorithms have substantially improved capabilities in this domain, facilitating real-time tracking and identification of the human form, thereby enabling security personnel to respond swiftly. This paper employs the Faster-RCNN algorithm for model training, utilizing the Information and Automation Research (INRIA) database. The deep learning-trained model proves highly accurate in human body detection, effectively recognizing human movements and behaviors. Such capabilities hold immense potential for implementation within the security sphere, including video surveillance systems and other similar applications where effectiveness is crucial.

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Section: Introductionmentioning

confidence: 99%

Deploying human body detection technologies in security systems: An in-depth study of the FASTER-GCNN algorithm

Jiang

2024

ACE

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Advanced Applications of Industrial Robotics: New Trends and Possibilities

et al. 2021

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This review is dedicated to the advanced applications of robotic technologies in the industrial field. Robotic solutions in areas with non-intensive applications are presented, and their implementations are analysed. We also provide an overview of survey publications and technical reports, classified by application criteria, and the development of the structure of existing solutions, and identify recent research gaps. The analysis results reveal the background to the existing obstacles and problems. These issues relate to the areas of psychology, human nature, special artificial intelligence (AI) implementation, and the robot-oriented object design paradigm. Analysis of robot applications shows that the existing emerging applications in robotics face technical and psychological obstacles. The results of this review revealed four directions of required advancement in robotics: development of intelligent companions; improved implementation of AI-based solutions; robot-oriented design of objects; and psychological solutions for robot–human collaboration.

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Faster R-CNN for Detecting Regions in Human-Annotated Micrograph Images

Cited by 2 publications

References 14 publications

Deploying human body detection technologies in security systems: An in-depth study of the FASTER-GCNN algorithm

Deploying human body detection technologies in security systems: An in-depth study of the FASTER-GCNN algorithm

Advanced Applications of Industrial Robotics: New Trends and Possibilities

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