# Deep Learning for Large-Scale Traffic-Sign Detection and Recognition

arXiv: https://arxiv.org/abs/1904.00649 · HTML: https://mlsanity.com/paper/1904.00649/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2019.

## Built on by (most cited first, 30 of 34)

- [Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey](https://mlsanity.com/paper/2307.04370/) (2023)
- [Closed-loop Analysis of Vision-based Autonomous Systems: A Case Study](https://mlsanity.com/paper/2302.04634/) (2023)
- [Unleashing the Power of Self-Supervised Image Denoising: A Comprehensive Review](https://mlsanity.com/paper/2308.00247/) (2023)
- [DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach](https://mlsanity.com/paper/2406.06986/) (2024)
- [NumbOD: A Spatial-Frequency Fusion Attack Against Object Detectors](https://mlsanity.com/paper/2412.16955/) (2024)
- [Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition](https://mlsanity.com/paper/2205.14315/) (2022)
- [DeepCert: Verification of Contextually Relevant Robustness for Neural Network Image Classifiers](https://mlsanity.com/paper/2103.01629/) (2021)
- [GLARE: A Dataset for Traffic Sign Detection in Sun Glare](https://mlsanity.com/paper/2209.08716/) (2022)
- [Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review](https://mlsanity.com/paper/2211.05120/) (2022)
- [Text-Aware Image Restoration with Diffusion Models](https://mlsanity.com/paper/2506.09993/) (2025)
- [Discrete-Event Controller Synthesis for Autonomous Systems with Deep-Learning Perception Components](https://mlsanity.com/paper/2202.03360/) (2022)
- [SignEye: Traffic Sign Interpretation from Vehicle First-Person View](https://mlsanity.com/paper/2411.11507/) (2024)
- [TSCLIP: Robust CLIP Fine-Tuning for Worldwide Cross-Regional Traffic Sign Recognition](https://mlsanity.com/paper/2409.15077/) (2024)
- [Towards Real-time Traffic Sign and Traffic Light Detection on Embedded Systems](https://mlsanity.com/paper/2205.02421/) (2022)
- [Traffic Sign Interpretation in Real Road Scene](https://mlsanity.com/paper/2311.10793/) (2023)
- [Scenario-based Compositional Verification of Autonomous Systems with Neural Perception](https://mlsanity.com/paper/2504.20942/) (2025)
- [Unified Diffusion Transformer for High-fidelity Text-Aware Image Restoration](https://mlsanity.com/paper/2512.08922/) (2025)
- [Dynamic loss balancing and sequential enhancement for road-safety assessment and traffic scene classification](https://mlsanity.com/paper/2211.04165/) (2022)
- [Traffic Sign Recognition Dataset and Data Augmentation](https://mlsanity.com/paper/2303.18037/) (2023)
- [Backdoor Mitigation in Deep Neural Networks via Strategic Retraining](https://mlsanity.com/paper/2212.07278/) (2022)
- [An intelligent modular real-time vision-based system for environment perception](https://mlsanity.com/paper/2303.16710/) (2023)
- [Subgraph Stationary Hardware-Software Inference Co-Design](https://mlsanity.com/paper/2306.17266/) (2023)
- [Optimized Detection and Classification on GTRSB: Advancing Traffic Sign Recognition with Convolutional Neural Networks](https://mlsanity.com/paper/2403.08283/) (2024)
- [One-Shot Domain Incremental Learning](https://mlsanity.com/paper/2403.16707/) (2024)
- [What Demands Attention in Urban Street Scenes? From Scene Understanding towards Road Safety: A Survey of Vision-driven Datasets and Studies](https://mlsanity.com/paper/2507.06513/) (2025)
- [Unleashing the Capabilities of Large Vision-Language Models for Intelligent Perception of Roadside Infrastructure](https://mlsanity.com/paper/2601.10551/) (2026)
- [Comparing the Machine Readability of Traffic Sign Pictograms in Austria and Germany](https://mlsanity.com/paper/2109.02362/) (2021)
- [Providing Error Detection for Deep Learning Image Classifiers Using Self-Explainability](https://mlsanity.com/paper/2210.08210/) (2022)
- [Road Traffic Sign Recognition method using Siamese network Combining Efficient-CNN based Encoder](https://mlsanity.com/paper/2502.15307/) (2025)
- [Adversarial Wear and Tear: Exploiting Natural Damage for Generating Physical-World Adversarial Examples](https://mlsanity.com/paper/2503.21164/) (2025)

## Abstract (arXiv)

Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory. It provides accurate and timely way to manage traffic-sign inventory with a minimal human effort. In the computer vision community the recognition and detection of traffic signs is a well-researched problem. A vast majority of existing approaches perform well on traffic signs needed for advanced drivers-assistance and autonomous systems. However, this represents a relatively small number of all traffic signs (around 50 categories out of several hundred) and performance on the remaining set of traffic signs, which are required to eliminate the manual labor in traffic-sign inventory management, remains an open question. In this paper, we address the issue of detecting and recognizing a large number of traffic-sign categories suitable for automating traffic-sign inventory management. We adopt a convolutional neural network (CNN) approach, the Mask R-CNN, to address the full pipeline of detection and recognition with automatic end-to-end learning. We propose several improvements that are evaluated on the detection of traffic signs and result in an improved overall performance. This approach is applied to detection of 200 traffic-sign categories represented in our novel dataset. Results are reported on highly challenging traffic-sign categories that have not yet been considered in previous works. We provide comprehensive analysis of the deep learning method for the detection of traffic signs with large intra-category appearance variation and show below 3% error rates with the proposed approach, which is sufficient for deployment in practical applications of traffic-sign inventory management.