Jul 2026· Information Technology and Control· Vol 55, pp. 858-879· 0 citations· 33 references
Computer Science
TL;DR
This research method outperforms existing mainstream models in terms of accuracy, efficiency, and interference tolerance, providing reliable technical support for real-time driving behavior monitoring.
Abstract
As a core interaction in the human-vehicle-road system, automated and refined detection of driving behavior has emerged as a crucial research direction in intelligent transportation systems and advanced driver assistance systems. Traditional post-event monitoring models that rely on manual or sensor-based methods are no longer able to meet the requirements of real-time and accurate risk identification. Therefore, this study proposes the You Only Look Once - Lightweight - BiFPN - ECA (YOLO-LBE) detection method. By integrating ghost convolution and GhostC2f modules to diminish computational complexity, the study employs a weighted bidirectional feature pyramid network, and further embed an ECA module to significantly enhance the precision and stability of driver behavior detection. Experimental findings demonstrate that the improved YOLOv8 model improves mAP@0.5 by 5.3%, FPS by 32.4%, Params by 34.4%, and FLOPs by 33.3% compared to YOLOv5s. This research method outperforms existing mainstream models in terms of accuracy, efficiency, and interference tolerance, providing reliable technical support for real-time driving behavior monitoring.
Driver’s distraction and fatigue are among the major contributing factors of
traffic accidents. This study presents a methodology to identify driver’s
distraction using a refined You Only Look Once (YOLO) model, denoted as
YOLOv11.To address the inconsistent performance of earlier versions of YOLO,
especially with regard to lack of systematic evaluations, this study proposes an
improved YOLOv11 model. A mixed local channel attention (MLCA) module is further
introduced to enhance small object feature extractions considering the use of
Wise-Intersection over Union (IoU) v3 loss function to improve localization
accuracy and training stability. Experiments demonstrated that this model
outperforms competing models across all metrics, achieving 99.13% mAP at 0.5 and
82.54% mAP at 0.5:0.95, while also achieving minimal bounding box loss. The
proposed model demonstrated higher accuracy and robustness, making it suitable
for real-world driver monitoring system (DMS) deployments.
Bao Ma, Hamid Taghavifar, Zhijun Fu et al.· SAE technical paper series· 0 citations
IYOLO, an enhanced YOLOv8-based framework for simultaneous detection and classification of vehicles, drivers, and passengers on highways, aiming to distinguish drivers from passengers and establish one-to-one vehicle-driver associations is proposed.
Yang Zhang, Peihua Lv, Hongjin Ren et al.· International Conference on...· 0 citations
Lane detection is a core perception task for Advanced Driver Assistance Systems (ADAS) and autonomous driving. Current methods struggle to balance accuracy, model complexity and inference efficiency: high-precision models rely on heavy modules with excessive computation, while lightweight ones suffer from weak feature extraction and low precision. To alleviate this inherent trade-off, we propose YOLOv8n-LaneDG based on YOLOv8n-seg. We design a dual-path gated fusion block to strengthen lane features and an efficient upsampling convolution block to reduce computational overhead, and we further design a weighted continuity loss to preserve lane structural integrity. Evaluated on TuSimple, our method lifts mAP@0.5 from 74.3% to 95.2%. It outperforms mainstream lightweight models and matches heavy YOLOv8s-seg with far fewer parameters, delivering a high-precision, deployable lane detection solution for vehicle-end platforms.
Ke Zheng, Jin-Cheng Jiang, Zhixue Liang et al.· Computers· 0 citations
This research establishes a statistically robust and deployable foundation for next-generation intelligent transportation systems by coupling Bayesian learning theory with edge computing design.
Seyed Mohammad Hosseini, V. Kiani, Hadi Sadoghi-Yazdi· Computing· 0 citations
Distracted driving is a key factor leading to traffic accidents. Timely identification and intervention of this behavior are of great significance for improving traffic safety. To this end, this paper proposes a driver distraction detection method based on the improved YOLOv11s. This method introduces an efficient multi-scale attention (EMA) mechanism in the neck network, enhancing the feature extraction capability through multi-scale feature extraction and cross-space learning architecture. To further improve detection accuracy, Inner-WIoU is adopted as the bounding box regression loss function. Its dynamic weight allocation strategy can assign higher loss weights to difficult-to-identify samples such as occlusion or abnormal postures, guiding the model to focus on these samples and effectively alleviating the problem of sample imbalance. Experimental results on the SFDDD distracted driving dataset show that the improved YOLOv11s algorithm significantly improves detection accuracy.
Xinghua Xia, Enhai Cui· International Conference on...· 0 citations
One of the leading causes of mortality worldwide is traffic accidents brought on by sleepy drivers, as the driver gradually loses focus without direct awareness. And although there are traditional techniques for monitoring the driver, most of them suffer from limited accuracy or slow response. We created a method to identify driver inattention using real-time video processing to quickly and accurately categorize driver information in response to the growing need for driver monitoring technology while driving.
The detection system relies on head tilt and eye movement indicators as key determinants of drowsiness thru real-time face detection using the YOLO algorithm. The high performance and ease of use of the YOLO algorithm helped identify signs of drowsiness with low response time and high precision. In this paper, we adopted the tenth version of the YOLO algorithm. YOLOv10 detects the driver's face. then facial landmarks and vital areas are extracted by MediaPipe FaceMesh. After that, the head rotation angles (Yaw, Pitch, Roll) and the eye closure ratio are calculated separately using computer vision algorithms to determine the driver's drowsiness. The results showed that the rapid face detection of the YOLO algorithm reduces errors and increases the system's reliability. Additionally, combining head movement indicators with eye indicators enhances the accuracy and speed of drowsiness detection compared to relying on a single indicator. The model was tested on the (DDD-NTHU) database from OpenCV. The system achieved an accuracy of 96% in identifying two states of the driver: alert or drowsy.
F. J. Kadhim· Al-Noor Journal of Engineeri...· 0 citations
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