Traffic Sign Detection

Latest papers

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

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 11, 2026J/K move · Enter open · S save

Squeeze-Aware-YOLO: Early Channel Recalibration and Multi-Scale Fusion for Real-Time Traffic Sign Detection

Mizbaul Haque Maruf, Syed Samin Sadaf, Syed Tamzid Bakht, Md. Hasanul Kabir+1

Dept. of Computer Science and Engineering, Islamic University of Technology, Boardbazar, Gazipur, Bangladesh

Traffic Sign Detection Object Detection Real-Time Object Detection Efficient Object Detection Multi-Scale Feature Fusion

Open paper AskPDF arXiv

Traffic sign detection plays a vital role in intelligent transportation systems, supporting the safe operation of advanced driver assistance systems and autonomous vehicles. Despite significant progress in real-time object detection, accurately identifying traffic signs under challenging conditions remains difficult. Variations in lighting, adverse weather, motion blur, and partial occlusion often reduce detection performance, especially for small or visually degraded signs where important spatial information can be lost. To address these challenges, we propose Squeeze-Aware-YOLO, a lightweight traffic sign detection framework that introduces a multi-branch bottleneck module combining convolutional kernels of different sizes with a Squeeze-and-Excitation mechanism to improve feature extraction and adaptive channel weighting. In addition, we integrate the Multi-Branch Auxiliary Feature Pyramid Network and Re-parameterized Heterogeneous Multi-Scale modules to strengthen multi-scale feature representation and feature fusion while preserving real-time inference efficiency for practical deployment. The proposed approach is evaluated on the Bangladeshi Traffic Sign Detection Dataset, the CSUST Chinese Traffic Sign Detection Benchmark 2021, and the German Traffic Sign Detection Benchmark, achieving [email protected] scores of 74.7%, 79.4%, and 84.0%, respectively, while consistently outperforming recent state-of-the-art YOLO-based traffic sign detectors. These findings demonstrate that combining multi-scale receptive fields, channel attention, and enhanced feature fusion enables more accurate and robust traffic sign detection while maintaining the computational efficiency required for real-world deployment.