Papers Traffic Sign Recognition
“Traffic Sign Recognition” 태그가 달린 논문 151편 · 필터 해제
Filtering-out poor-quality images for data preparation
Filtering noise is a fundamental part of data preparation that enhances image quality for applications such as object segmentation, detection, and recognition. Various noise reduction techniques are proposed in the liter…
Image Quality AssessmentTraffic Sign RecognitionObject SegmentationAutonomous VehiclesQDS-SNN: Energy-efficient Quantum Deeply-Supervised Spiking Neural Network Algorithm for Traffic Sign Recognition
Traffic sign recognition is crucial for intelligent transportation and autonomous driving, as it can improve driving efficiency and ensure road safety. However, traditional recognition methods are based on large datasets…
Traffic Sign RecognitionAutonomous DrivingHierarchically 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 DrivingVision-Based Lane Following and Traffic Sign Recognition for Resource-Constrained Autonomous Vehicles
Autonomous vehicles (AVs) rely on real-time perception systems to understand road environments and ensure safe navigation. However, implementing reliable perception algorithms on resource-constrained embedded platforms r…
Traffic Sign RecognitionAutonomous VehiclesAutonomous DrivingLane DetectionBeyond Corner Patches: Semantics-Aware Backdoor Attack in Federated Learning
Backdoor attacks on federated learning (FL) are most often evaluated with synthetic corner patches or out-of-distribution (OOD) patterns that are unlikely to arise in practice. In this paper, we revisit the backdoor thre…
Traffic Sign RecognitionFederated LearningMitigating Backdoor Attacks in Federated Learning Using PPA and MiniMax Game Theory
Federated Learning (FL) is witnessing wider adoption due to its ability to benefit from large amounts of scattered data while preserving privacy. However, despite its advantages, federated learning suffers from several s…
Traffic Sign RecognitionFederated LearningTraffic Sign Recognition in Autonomous Driving: Dataset, Benchmark, and Field Experiment
Traffic Sign Recognition (TSR) is a core perception capability for autonomous driving, where robustness to cross-region variation, long-tailed categories, and semantic ambiguity is essential for reliable real-world deplo…
Traffic Sign RecognitionAutonomous DrivingMulti-model approach for autonomous driving: A comprehensive study on traffic sign-, vehicle- and lane detection and behavioral cloning
Deep learning and computer vision techniques have become increasingly important in the development of self-driving cars. These techniques play a crucial role in enabling self-driving cars to perceive and understand their…
Traffic Sign RecognitionAutonomous DrivingTransfer LearningData AugmentationDriveCombo: Benchmarking Compositional Traffic Rule Reasoning in Autonomous Driving
Multimodal Large Language Models (MLLMs) are rapidly becoming the intelligence brain of end-to-end autonomous driving systems. A key challenge is to assess whether MLLMs can truly understand and follow complex real-world…
Traffic Sign RecognitionAutonomous DrivingScene GenerationVisual ReasoningPhysical Evaluation of Naturalistic Adversarial Patches for Camera-Based Traffic-Sign Detection
This paper studies how well Naturalistic Adversarial Patches (NAPs) transfer to a physical traffic sign setting when the detector is trained on a customized dataset for an autonomous vehicle (AV) environment. We construc…
Traffic Sign RecognitionFrom Snow to Rain: Evaluating Robustness, Calibration, and Complexity of Model-Based Robust Training
Robustness to natural corruptions remains a critical challenge for reliable deep learning, particularly in safety-sensitive domains. We study a family of model-based training approaches that leverage a learned nuisance v…
Traffic Sign RecognitionData AugmentationRectifying Adversarial Examples Using Their Vulnerabilities
Deep neural network-based classifiers are prone to errors when processing adversarial examples (AEs). AEs are minimally perturbed input data undetectable to humans posing significant risks to security-dependent applicati…
Traffic Sign RecognitionAutonomous DrivingSynset Signset Germany: a Synthetic Dataset for German Traffic Sign Recognition
In this paper, we present a synthesis pipeline and dataset for training / testing data in the task of traffic sign recognition that combines the advantages of data-driven and analytical modeling: GAN-based texture genera…
Traffic Sign RecognitionMeasuring the Effect of Background on Classification and Feature Importance in Deep Learning for AV Perception
Common approaches to explainable AI (XAI) for deep learning focus on analyzing the importance of input features on the classification task in a given model: saliency methods like SHAP and GradCAM are used to measure the …
Traffic Sign RecognitionFeature ImportanceBenchmarking Nighttime Traffic Sign Recognition with Illumination-Adaptive Detection and Semantic Attribute Reasoning
Traffic signboards are vital for road safety and intelligent transportation systems. Yet, recognizing traffic signs at night remains underexplored due to the scarcity of real-world public datasets capturing low-light deg…
Traffic Sign RecognitionAutonomous DrivingT2I-Based Physical-World Appearance Attack against Traffic Sign Recognition Systems in Autonomous Driving
Traffic Sign Recognition (TSR) systems play a critical role in Autonomous Driving (AD) systems, enabling real-time detection of road signs, such as STOP and speed limit signs. While these systems are increasingly integra…
Traffic Sign RecognitionAutonomous DrivingTrapped by Their Own Light: Deployable and Stealth Retroreflective Patch Attacks on Traffic Sign Recognition Systems
Traffic sign recognition plays a critical role in ensuring safe and efficient transportation of autonomous vehicles but remain vulnerable to adversarial attacks using stickers or laser projections. While existing attack …
Traffic Sign RecognitionAutonomous VehiclesInformation-Theoretic Greedy Layer-wise Training for Traffic Sign Recognition
Modern deep neural networks (DNNs) are typically trained with a global cross-entropy loss in a supervised end-to-end manner: neurons need to store their outgoing weights; training alternates between a forward pass (compu…
Traffic Sign RecognitionVISAT: Benchmarking Adversarial and Distribution Shift Robustness in Traffic Sign Recognition with Visual Attributes
We present VISAT, a novel open dataset and benchmarking suite for evaluating model robustness in the task of traffic sign recognition with the presence of visual attributes. Built upon the Mapillary Traffic Sign Dataset …
Traffic Sign RecognitionMulti-Task LearningAdversarial AttackAutonomous DrivingEvaluating the Impact of Adversarial Attacks on Traffic Sign Classification using the LISA Dataset
Adversarial attacks pose significant threats to machine learning models by introducing carefully crafted perturbations that cause misclassification. While prior work has primarily focused on MNIST and similar datasets, t…
Traffic Sign Recognition