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作 者:Yahia Said YahyaAlassaf Refka Ghodhbani Yazan Ahmad Alsariera Taoufik Saidani Olfa Ben Rhaiem Mohamad Khaled Makhdoum Manel Hleili
机构地区:[1]Department of Electrical Engineering,College of Engineering,Northern Border University,Arar,91431,Saudi Arabia [2]Department of Civil Engineering,College of Engineering,Northern Border University,Arar,91431,Saudi Arabia [3]Faculty of Computing and Information Technology,Northern Border University,Rafha,91911,Saudi Arabia [4]College of Science,Northern Border University,Arar,91431,Saudi Arabia [5]Department of Mathematics,Faculty of Sciences of Tabuk,University of Tabuk,Tabuk,71491,Saudi Arabia
出 处:《Computer Modeling in Engineering & Sciences》2024年第10期733-749,共17页工程与科学中的计算机建模(英文)
基 金:funded by the Deanship of Scientific Research at Northern Border University,Arar,Kingdom of Saudi Arabia through Research Group No.(RG-NBU-2022-1234).
摘 要:Enhancing road safety globally is imperative,especially given the significant portion of traffic-related fatalities attributed to motorcycle accidents resulting from non-compliance with helmet regulations.Acknowledging the critical role of helmets in rider protection,this paper presents an innovative approach to helmet violation detection using deep learning methodologies.The primary innovation involves the adaptation of the PerspectiveNet architecture,transitioning from the original Res2Net to the more efficient EfficientNet v2 backbone,aimed at bolstering detection capabilities.Through rigorous optimization techniques and extensive experimentation utilizing the India driving dataset(IDD)for training and validation,the system demonstrates exceptional performance,achieving an impressive detection accuracy of 95.2%,surpassing existing benchmarks.Furthermore,the optimized PerspectiveNet model showcases reduced computational complexity,marking a significant stride in real-time helmet violation detection for enhanced traffic management and road safety measures.
关 键 词:Non-helmet use detection traffic violation SAFETY deep learning optimized PerspectiveNet
分 类 号:TP18[自动化与计算机技术—控制理论与控制工程]
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