Papers Traffic Sign Detection
“Traffic Sign Detection” 태그가 달린 논문 61편 · 필터 해제
Multi-Modal Traffic Sign Detection with Semantic Attributes for Autonomous Driving
Reliable traffic sign detection is a prerequisite for the global deployment of autonomous driving systems, where regulatory compliance and road safety depend on perceiving signs correctly across regions, ranges, and weat…
Traffic Sign DetectionAutonomous DrivingObject DetectionHierarchically Decoupled Mixture-of-Experts for Robust Traffic Sign Recognition in Complex Driving Scenarios
Traffic sign detection is a fundamental component of environmental perception in autonomous driving and intelligent transportation systems. However, most existing detectors rely on static inference with globally shared p…
Traffic Sign RecognitionTraffic Sign DetectionAutonomous DrivingEfficient Cross-Country Data Acquisition Strategy for ADAS via Street-View Imagery
Deploying ADAS and ADS across countries remains challenging due to differences in legislation, traffic infrastructure, and visual conventions, which introduce domain shifts that degrade perception performance. Traditiona…
Traffic Sign DetectionArgos: A Decentralized Federated System for Detection of Traffic Signs in CAVs
Connected and automated vehicles generate vast amounts of sensor data daily, raising significant privacy and communication challenges for centralized machine learning approaches in perception tasks. This study presents a…
Traffic Sign DetectionFederated LearningContrastive Learning-Driven Traffic Sign Perception: Multi-Modal Fusion of Text and Vision
Traffic sign recognition, as a core component of autonomous driving perception systems, directly influences vehicle environmental awareness and driving safety. Current technologies face two significant challenges: first,…
Traffic Sign RecognitionTraffic Sign DetectionContrastive LearningAutonomous DrivingYOLO-LLTS: Real-Time Low-Light Traffic Sign Detection via Prior-Guided Enhancement and Multi-Branch Feature Interaction
Detecting traffic signs effectively under low-light conditions remains a significant challenge. To address this issue, we propose YOLO-LLTS, an end-to-end real-time traffic sign detection algorithm specifically designed …
Small Object DetectionTraffic Sign DetectionYOLO-CCA: A Context-Based Approach for Traffic Sign Detection
Traffic sign detection is crucial for improving road safety and advancing autonomous driving technologies. Due to the complexity of driving environments, traffic sign detection frequently encounters a range of challenges…
Autonomous DrivingTraffic Sign DetectionYOLO-TS: Real-Time Traffic Sign Detection with Enhanced Accuracy Using Optimized Receptive Fields and Anchor-Free Fusion
Ensuring safety in both autonomous driving and advanced driver-assistance systems (ADAS) depends critically on the efficient deployment of traffic sign recognition technology. While current methods show effectiveness, th…
Autonomous Drivingobject-detectionObject DetectionTraffic Sign Detection+1Human-in-the-loop Reasoning For Traffic Sign Detection: Collaborative Approach Yolo With Video-llava
Traffic Sign Recognition (TSR) detection is a crucial component of autonomous vehicles. While You Only Look Once (YOLO) is a popular real-time object detection algorithm, factors like training data quality and adverse we…
Autonomous Vehiclesobject-detectionObject DetectionReal-Time Object Detection+2FUSED-Net: Detecting Traffic Signs with Limited Data
Automatic Traffic Sign Recognition is paramount in modern transportation systems, motivating several research endeavors to focus on performance improvement by utilizing large-scale datasets. As the appearance of traffic …
Data AugmentationDomain AdaptationFew-Shot Object Detectionobject-detection+3Accurate Automatic 3D Annotation of Traffic Lights and Signs for Autonomous Driving
3D detection of traffic management objects, such as traffic lights and road signs, is vital for self-driving cars, particularly for address-to-address navigation where vehicles encounter numerous intersections with these…
Autonomous DrivingSelf-Driving CarsTraffic Sign DetectionYOLO-PPA based Efficient Traffic Sign Detection for Cruise Control in Autonomous Driving
It is very important to detect traffic signs efficiently and accurately in autonomous driving systems. However, the farther the distance, the smaller the traffic signs. Existing object detection algorithms can hardly det…
Autonomous Drivingobject-detectionObject DetectionTraffic Sign DetectionEMDFNet: Efficient Multi-scale and Diverse Feature Network for Traffic Sign Detection
The detection of small objects, particularly traffic signs, is a critical subtask within object detection and autonomous driving. Despite the notable achievements in previous research, two primary challenges persist. Fir…
Autonomous DrivingNovel Object Detectionobject-detectionObject Detection+1Cross-domain Few-shot In-context Learning for Enhancing Traffic Sign Recognition
Recent multimodal large language models (MLLM) such as GPT-4o and GPT-4v have shown great potential in autonomous driving. In this paper, we propose a cross-domain few-shot in-context learning method based on the MLLM fo…
Autonomous DrivingCross-Domain Few-ShotIn-Context LearningTraffic Sign Detection+1Voice-Assisted Real-Time Traffic Sign Recognition System Using Convolutional Neural Network
Traffic signs are important in communicating information to drivers. Thus, comprehension of traffic signs is essential for road safety and ignorance may result in road accidents. Traffic sign detection has been a researc…
Autonomous Vehiclestext-to-speechText to SpeechTraffic Sign Detection+1Optimized Detection and Classification on GTRSB: Advancing Traffic Sign Recognition with Convolutional Neural Networks
In the rapidly evolving landscape of transportation, the proliferation of automobiles has made road traffic more complex, necessitating advanced vision-assisted technologies for enhanced safety and navigation. These tech…
Autonomous DrivingAutonomous VehiclesTraffic Sign DetectionTraffic Sign RecognitionLearning to Defer to a Population: A Meta-Learning Approach
The learning to defer (L2D) framework allows autonomous systems to be safe and robust by allocating difficult decisions to a human expert. All existing work on L2D assumes that each expert is well-identified, and if any …
Meta-LearningTraffic Sign DetectionEfficient Vision Transformer for Accurate Traffic Sign Detection
This research paper addresses the challenges associated with traffic sign detection in self-driving vehicles and driver assistance systems. The development of reliable and highly accurate algorithms is crucial for the wi…
Autonomous DrivingInductive Biasobject-detectionObject Detection+2Real-Time Traffic Sign Detection: A Case Study in a Santa Clara Suburban Neighborhood
This research project aims to develop a real-time traffic sign detection system using the YOLOv5 architecture and deploy it for efficient traffic sign recognition during a drive in a suburban neighborhood. The project's …
Autonomous DrivingManagementTraffic Sign DetectionTraffic Sign RecognitionCCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme Conditions
Traffic sign detection is an important research direction in intelligent driving. Unfortunately, existing methods often overlook extreme conditions such as fog, rain, and motion blur. Moreover, the end-to-end training st…
DenoisingImage Denoisingobject-detectionObject Detection+1