# Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images

## TL;DR

This paper introduces a novel data generation method for traffic sign detection that uses only arbitrary natural images and sign templates, eliminating the need for real-world training data and effectively addressing data scarcity and imbalance issues.

## Contribution

The authors propose a simple, domain-agnostic data synthesis approach for training traffic sign detectors without real images, achieving high accuracy and robustness.

## Key findings

- Achieved 95.66% mAP on German traffic signs.
- Detector precision, recall, and F1-score are approximately 94%, 91%, and 93%.
- Training with synthetic data without real background images is surprisingly effective.

## Abstract

Deep learning has been successfully applied to several problems related to autonomous driving. Often, these solutions rely on large networks that require databases of real image samples of the problem (i.e., real world) for proper training. The acquisition of such real-world data sets is not always possible in the autonomous driving context, and sometimes their annotation is not feasible (e.g., takes too long or is too expensive). Moreover, in many tasks, there is an intrinsic data imbalance that most learning-based methods struggle to cope with. It turns out that traffic sign detection is a problem in which these three issues are seen altogether. In this work, we propose a novel database generation method that requires only (i) arbitrary natural images, i.e., requires no real image from the domain of interest, and (ii) templates of the traffic signs, i.e., templates synthetically created to illustrate the appearance of the category of a traffic sign. The effortlessly generated training database is shown to be effective for the training of a deep detector (such as Faster R-CNN) on German traffic signs, achieving 95.66% of mAP on average. In addition, the proposed method is able to detect traffic signs with an average precision, recall and F1-score of about 94%, 91% and 93%, respectively. The experiments surprisingly show that detectors can be trained with simple data generation methods and without problem domain data for the background, which is in the opposite direction of the common sense for deep learning.