ArticleSensors (Basel, Switzerland)2025
Perimeter Security Utilizing Thermal Object Detection.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
4 authors.
Funding
Abstract
In recent years, an increasing interest in artificial intelligence applications in a widespread spectrum of fields which include, among others, robotics, communications, artistic creations, security and protection technologies, etc., has been observed. Of the latter categories, one field which has largely benefitted is surveillance and security technologies. This fact is combined with an increase in omnipresent automatic surveillance system installations which pave the way to new technologies. Technologies that are being promoted are the ones offering uninterrupted, robust, efficient and reliable operation. In this work, we examine the ability of thermal automatic detection systems to fulfill their role as an essential part of such a mechanism. The primary advantage of thermal detection is the potential to provide a 24-h uninterrupted detection service exploiting its innate robustness against environmental or weather changes and shifts in illumination conditions. For providing a reliable security mechanism, a second requirement is considered sine qua non: the efficiency of the system in order to provide timely alerts for potential threats and incidents. In this work, we evaluate various efficient object detection models operating solely in the thermal/infrared spectrum to examine their role as potential backbone detectors in surveillance systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.