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Traffic Sign Recognition for Autonomous Driving Using Branched YOLOv2 and Geometric Features

Paper recorded by Signals 4 on 2026-09-18 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-18 on arXiv · recorded by Signals 4 on 2026-09-21

Category: cs.CV · 计算机视觉 · first seen 2026-09-21

Abstract

Traffic sign recognition (TSR) is an important perception task for autonomous driving and advanced driver-assistance systems, where a system must both localize traffic signs and determine their semantic classes efficiently. This work presents a TSR system based on YOLOv2 for simultaneous detection and classification. Two complementary modifications are studied. First, YOLOv2 is extended with inter

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#18 most recent of 270 cs.CV papers we have recorded · ↑ newer: OmniVBench: A Benchmark and Large-Scale Dataset for Omni Reference-to- · ↓ older: PRIME: Perception Feedback with Situational Memory Embeddings in VLA M
Cite this page: Traffic Sign Recognition for Autonomous Driving Using Branched YOLOv2 and Geometric Features: the #18 most recent of 270 cs.CV papers we have recorded (as of 2026-09-18). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/traffic-sign-recognition-for-autonomous-driving-using-branched-yolov2-and-geomet.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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