Aug 2026· International Conference on Digital Image Processing· Vol 14351, pp. 143510X - 143510X-11· 0 citations· 22 references
Engineering
TL;DR
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.
Abstract
In recent years, vehicle and driver detection in real-world traffic scenarios has attracted increasing attention, with accurate driver identification being critical for traffic supervision and public safety. This paper proposes 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. The model leverages Cross Stage Partial to Fast (C2f) modules to reduce redundant computations and accelerate inference, and employs an optimized feature pyramid with multi-scale fusion to improve small-object detection. An adaptive Label Smoothing Regularization strategy enhances generalization and classification robustness, while Online Hard Sample Mining focuses learning on challenging samples during training, improving feature discrimination and overall performance under complex conditions. Extensive experiments on the PSD-HIGHROAD dataset demonstrate that IYOLO consistently outperforms state-of-the-art methods in detecting and classifying vehicles, drivers, and passengers, achieving superior accuracy and robustness across varying lighting, poses, and traffic conditions.
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
Current road safety systems face challenges in detecting curved roads and vehicles under varying lighting and weather conditions, leading to lane departure and collision risks. To address this, we propose CurvLaneNet-YOLO, a deep learning framework based on YOLOv8 that simultaneously detects road curvature and vehicles in lanes. The system integrates a parallel polynomial lane detection head into the YOLOv8 architecture, enabling real-time curvature estimation alongside vehicle detection. This research develops an AI-enabled real-time monitoring system to substantially improve road safety. The system makes use of the enhanced capabilities and features of the YOLOv8 model, which involves data preparation, model training, extensive testing, and data augmentation to guarantee model precision. Main goals are estimation of road curvature in real-time, high-performance processing and better assist for the driver. To assess the research, the Cars sample from KITTI dataset on Kaggle, which includes 7,481 images of 640 × 640 resolution, were used. The research achieves an inference latency of 15.4 ± 0.3 ms/image on the test hardware, with mAP@0.5 = 0.9373 and mAP@0.5:0.95 = 0.9217, indicating significant improvements over existing work. This research demonstrates the feasibility of using deep learning techniques for vehicle detection and road curvature estimation in real-time. The results presented in this study show promising potential to integrate into a driver-assistance system, although the data used here is limited to a proof-of-concept validation on the KITTI dataset. The suggested solution is competitive in terms of accuracy and inference time on embedded hardware, providing a potential roadmap for its application in real-world scenarios within the context of intelligent transportation systems.
Amit Pimpalkar, Pranali Dandekar, Harika Vanam et al.· Scientific Reports· 0 citations
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.
Guozhu Sui, Meixia Song, Hai-Yun Sun et al.· Information Technology and C...· 0 citations
An effective real-time traffic accident detection framework based on YOLOv8 that can be implemented in intelligent transportation systems, traffic surveillance platforms, and advanced driver assistance applications is proposed.
Chuwe Ashlet Munashe, Chaoyu Yang· International Journal of Sci...· 0 citations
Autonomous vehicles depend on fast and reliable perception systems to detect surrounding vehicles, pedestrians, cyclists, traffic signs, and other road objects in real time. This paper presents a comprehensive survey and analysis of one-stage object detectors for autonomous driving rather than an implementation of a new detection system. The survey reviews the evolution of major one-stage detectors, including YOLOv1, SSD, RetinaNet, EfficientDet, anchor-free detectors such as FCOS and CenterNet, and recent real-time models such as YOLOv10. It compares these architectures through their design choices, feature-fusion strategies, loss functions, deployment trade-offs, and reported benchmark performance. The paper also summarizes commonly used autonomous-driving datasets, evaluation metrics, open challenges, and future research directions. Overall, this survey highlights how one-stage detectors balance speed, accuracy, efficiency, and robustness, while also emphasizing the remaining gap between benchmark results and dependable real-world autonomous-driving performance.
Jonel Roman, Ryan Sirjue, Peter Nguyen et al.· 0 citations
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
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