arXiv AI
RIECNN: Real-Time Image Enhanced CNN for Traffic Sign Recognition
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jul 28
Multi-model approach for autonomous driving: A comprehensive study on traffic sign-, vehicle- and lane detection and behavioral cloning
arXiv:2603. 09255v2 Announce Type: replace-cross Abstract: Deep learning and computer vision techniques have become increasingly important in the development of self-driving cars.
arXiv Computer Vision
Sep 21
Traffic Sign Recognition for Autonomous Driving Using Branched YOLOv2 and Geometric Features
The paper presents a traffic sign recognition system that extends YOLOv2 with a branched architecture and geometric feature integration. By adding intermediate prediction layers, the model can terminate inference early for easy cases, reducing computation time, while unsupervised Bayesian segmentation supplies geometric templates to improve classification of visually similar signs. Experiments on a combined GTSDB/GTSRB dataset show that the branched model achieves 0.680 mAP in 0.647 s, and adding geometric verification during inference raises mAP to 0.713.
arXiv Machine Learning
Aug 11
Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles
arXiv:2608. 08815v1 Announce Type: new Abstract: Traffic sign recognition (TSR) models based on deep neural networks achieve strong clean-data performance but remain vulnerable to physically realizable adversarial attacks, including shadow perturbations, natural-light interference, and printed patches.
arXiv AI
Sep 24
SMDDFNet: State-space Modeling and Dynamic Dual Fusion Network for Traffic Sign Detection
SMDDFNet is a deep learning detector designed for traffic sign images, addressing challenges such as small objects, scale variation, and occlusion. It combines a Dynamic Dual Fusion (DDF) module—integrating multi-scale attention and frequency‑domain dynamic filtering—with a state‑space modeling backbone that captures long‑range dependencies efficiently. A multi‑scale feature fusion neck further aggregates pyramid features, enabling robust localization of small signs while maintaining real‑time throughput on datasets like TT100K, GTSDB, PASCAL VOC, and Roboflow.
Hugging Face Trending Papers
Jul 14
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 literature, including the use of median, Gaussian, and bilateral filters.
arXiv AI
Jul 7
A Transformer-Based Contrastive Learning Approach for Few-Shot Sign Language Recognition
arXiv:2204. 02803v2 Announce Type: replace-cross Abstract: Sign language recognition from monocular video or 2D pose sequences is challenging, both because 3D information must be inferred from 2D observations and because the signal is inherently spatiotemporal.