Real-Time Object Detection in Road Traffic with Road Maintenance Capability
Lecture Notes in Networks and Systems, ISSN: 2367-3389, Vol: 1024 LNNS, Page: 491-502
2024
- 1Citations
- 2Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
In an epoch characterized by urbanization and an escalating reliance on road networks, the imperatives of road safety and infrastructure preservation ascend to preeminent significance. Object detection, underpinned by breakthroughs in computer vision and machine learning, assumes a pivotal role in these domains. It is used for looking after real-time traffic by recognizing different vehicles, road objects, and anomalies merging it with safety and management of traffic flow. Along with that, it provides prior detection architectures for road obstacles. This research deals with object detection algorithms which allows both in-market and standard techniques which include neural networking concepts suitable for acting efficiently to the objects detected.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85202736800&origin=inward; http://dx.doi.org/10.1007/978-981-97-3817-5_35; https://link.springer.com/10.1007/978-981-97-3817-5_35; https://dx.doi.org/10.1007/978-981-97-3817-5_35; https://link.springer.com/chapter/10.1007/978-981-97-3817-5_35
Springer Science and Business Media LLC
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