Traffic Sign Recognition
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Benchmarks
GTSRB
Tsinghua-Tencent 100K
DFG traffic-sign dataset
BelgaLogos
FlickrLogos-32
TopLogo-10
Most implemented
Architecturing Binarized Neural Networks for Traffic Sign Recognition
Novel Deep Learning Model for Traffic Sign Detection Using Capsule Networks
Filtering-out poor-quality images for data preparation
Targeted Physical-World Attention Attack on Deep Learning Models in Road Sign Recognition
Papers
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 Learning