Deep-Learning-Enhanced Multitarget Detection for End–Edge–Cloud Surveillance in Smart IoT
Impact in
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- IEEE Internet of Things Journal
In The Last Decade
doi.org/10.1109/jiot.2021.3077449 →Countries where authors are citing Deep-Learning-Enhanced Multitarget Detection for End–Edge–Cloud Surveillance in Smart IoT
This map shows the geographic impact of Deep-Learning-Enhanced Multitarget Detection for End–Edge–Cloud Surveillance in Smart IoT. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Deep-Learning-Enhanced Multitarget Detection for End–Edge–Cloud Surveillance in Smart IoT with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deep-Learning-Enhanced Multitarget Detection for End–Edge–Cloud Surveillance in Smart IoT more than expected).
Fields of papers citing Deep-Learning-Enhanced Multitarget Detection for End–Edge–Cloud Surveillance in Smart IoT
This network shows the impact of Deep-Learning-Enhanced Multitarget Detection for End–Edge–Cloud Surveillance in Smart IoT. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Deep-Learning-Enhanced Multitarget Detection for End–Edge–Cloud Surveillance in Smart IoT.
About Deep-Learning-Enhanced Multitarget Detection for End–Edge–Cloud Surveillance in Smart IoT
This paper, published in 2021, received 270 indexed citations . Written by Xiaokang Zhou, Xuesong Xu, Wei Liang, Zhi Zeng and Zheng Yan covering the research area of Safety, Risk, Reliability and Quality and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Artificial Intelligence (109 citations), Computer Networks and Communications (81 citations), Computer Vision and Pattern Recognition (70 citations), Information Systems (59 citations) and Electrical and Electronic Engineering (35 citations). Published in IEEE Internet of Things Journal.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.1109/jiot.2021.3077449.