Traffic Sign Detection

Latest papers

10 papers2 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Edge Deployable Lightweight Framework for Real-Time Driver Safety System with Integrated Lane Departure and Traffic Sign Recognition
Oct 18, 2026Hassaan Ali Qureshi, Danish Hussain, Ali Hassan +5Traffic Sign Detection Autonomous Driving Perception
Department of Mechatronics Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan · Department of Robotics and Artificial Intelligence, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan · Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan +2
Squeeze-Aware-YOLO: Early Channel Recalibration and Multi-Scale Fusion for Real-Time Traffic Sign Detection
Oct 11, 2026Mizbaul Haque Maruf, Syed Samin Sadaf, Syed Tamzid Bakht +2Traffic Sign Detection Object Detection
Dept. of Computer Science and Engineering, Islamic University of Technology, Boardbazar, Gazipur, Bangladesh
Traffic Sign Recognition for Autonomous Driving Using Branched YOLOv2 and Geometric Features
Sep 18, 2026Arefeh RezaeiTraffic Sign Detection Autonomous Driving Perception
Department of Computer Engineering, K. N. Toosi University of Technology
Structured-Prior-Guided Diffusion Inpainting with Physical Consistency for Traffic Sign Augmentation
Sep 2, 2026Luo Li, Chongchong Huang, Jun Jia +4Traffic Sign Detection Synthetic Data Generation
AMAP, Alibaba Group Beijing, China
Multi-Modal Traffic Sign Detection with Semantic Attributes for Autonomous Driving
Aug 21, 2026Meda Lazar, Sourab Sridhar, Shashwata Gupta +3Traffic Sign Detection Autonomous Driving Perception
Arriver System Software S.r.l., Romania · Qualcomm Auto Ltd Sweden Filial · Qualcomm Auto Ltd., UK +1
Hierarchically Decoupled Mixture-of-Experts for Robust Traffic Sign Recognition in Complex Driving Scenarios
Jun 1, 2026Mingxiao Wang, Xiaozhen Qu, Bolin Gao +2Traffic Sign Detection Sparse Mixture-of-Experts
School of Automotive and Traﬀic Engineering, Liaoning University of Technology, Jinzhou, Liaoning 121001, China. · State Key Laboratory of Intelligent Green Vehicles and Mobility, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China.
SignReasoner: Compositional Reasoning for Complex Traffic Sign Understanding via Functional Structure Units
Apr 12, 2026Ruibin Wang, Zhenyu Lin, Xinhai ZhaoTraffic Sign Detection Compositional Reasoning in VLMs
Huawei Noah’s Ark Lab
Physical Evaluation of Naturalistic Adversarial Patches for Camera-Based Traffic-Sign Detection
Feb 27, 2026Brianna D'Urso, Tahmid Hasan Sakib, Syed Rafay Hasan +1Traffic Sign Detection Physical Adversarial Attacks
Department of Computing Sciences, University of Hartford, West Hartford, CT, USA · Department of Electrical and Computer Engineering, Tennessee Technological University, Cookeville, TN, USA · Center for Manufacturing Research, Tennessee Technological University, Cookeville, TN, USA
Benchmarking Nighttime Traffic Sign Recognition with Illumination-Adaptive Detection and Semantic Attribute Reasoning
Nov 21, 2025Aditya Mishra, Akshay Agarwal, Haroon LoneTraffic Sign Detection Intelligent Transportation Systems
IISER Bhopal
SMDDFNet: State-space Modeling and Dynamic Dual Fusion Network for Traffic Sign Detection
May 2, 2025TianYi Yu, DaJian Zhong, Lilin WangTraffic Sign Detection Small Object Detection
Collage of Infomation Engineering, Shanghai Maritime University, Shanghai, 201306, China

Oct 18, 2026J/K move · Enter open · S save

Edge Deployable Lightweight Framework for Real-Time Driver Safety System with Integrated Lane Departure and Traffic Sign Recognition

Hassaan Ali Qureshi, Danish Hussain, Ali Hassan, Aon Safdar+4

Department of Mechatronics Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan · Department of Robotics and Artificial Intelligence, School of Mechanical and Manufacturing Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan · Department of Computer and Software Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan+2

Traffic Sign Detection Autonomous Driving Perception Edge Inference Lane Detection

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Lane departure and traffic-sign negligence are leading contributors to road accidents, particularly in regions with non-standardized signage and limited annotated data. Existing Advanced Driver Assistance Systems (ADAS) solutions are often developed as disjointed submodules, are rarely validated for real-time operation, and struggle to balance accuracy with low-latency inference for embedded deployment. We address these gaps with a unified, edge-deployable ADAS framework that simultaneously performs lane-departure warning (LDW) and traffic-sign detection and recognition (TSD/TSR), and validate the proposed system on automotive-class devices. Our framework combines a lightweight, lane-segmentation-driven LDW, which includes a drivable-area fallback for degraded markings, with a two-stage TSD/TSR pipeline consisting of efficient sign localization followed by fine-grained classification, designed to preserve accuracy at embedded latency. To support region-specific deployment under data scarcity, we curate a 35-class local traffic-sign dataset and introduce a generalized augmentation and balancing profile, expanding the corpus from 359 images to 8,750 samples. On an NVIDIA Jetson Orin Nano, the integrated system sustains 15 FPS end to end (32 FPS for LDW and 18 FPS for TSD/TSR). The TSD/TSR branch achieves 97.4 mAP for detection and 95.6% validation accuracy (93.1% on a local real-world set) while maintaining an approximately 13.9 M parameter footprint. Relative to recent methods that target a single task or report results only on desktop-class GPUs, our approach delivers both LDW and TSD/TSR with real-time performance on edge hardware, providing a practical pathway to low-cost, field-ready ADAS in resource-constrained settings.