TY  - EJOU
AU  - Said, Yahia 
AU  - Alassaf, Yahya 
AU  - Ghodhbani, Refka 
AU  - Alsariera, Yazan Ahmad 
AU  - Saidani, Taoufik 
AU  - Rhaiem, Olfa Ben 
AU  - Makhdoum, Mohamad Khaled 
AU  - Hleili, Manel 

TI  - AI-Based Helmet Violation Detection for Traffic Management System
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2024
VL  - 141
IS  - 1
SN  - 1526-1506

AB  - 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.
KW  - Non-helmet use detection; traffic violation; safety; deep learning; optimized PerspectiveNet

DO  - 10.32604/cmes.2024.052369