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

## TL;DR

This paper presents a deep learning approach using Mask R-CNN for large-scale traffic sign detection and recognition, addressing over 200 categories with high accuracy to improve traffic-sign inventory management.

## Contribution

It introduces an end-to-end CNN-based method with improvements tailored for large-scale traffic sign recognition, covering a broader set of categories than previous works.

## Key findings

- Achieved below 3% error rates on challenging traffic sign categories
- Successfully detected and recognized 200 traffic sign categories
- Demonstrated practical applicability for traffic-sign inventory management

## Abstract

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.