Deep Traffic Sign Detection and Recognition Without Target Domain Real Images

TL;DR

This paper introduces a method to generate synthetic traffic sign datasets from natural images and templates, enabling effective training of deep detectors without real target-domain images, thus addressing data scarcity and imbalance issues in autonomous driving.

Contribution

The authors propose a novel database generation technique that creates synthetic training data from natural images and templates, eliminating the need for real target-domain images.

Findings

Synthetic data training nearly matches real data performance on large datasets.

Training with synthetic images improves accuracy by 12.25% over smaller real datasets.

The method enhances detector performance even when target-domain data are available.

Abstract

Deep learning has been successfully applied to several problems related to autonomous driving, often relying on large databases of real target-domain images for proper training. The acquisition of such real-world data is not always possible in the self-driving context, and sometimes their annotation is not feasible. Moreover, in many tasks, there is an intrinsic data imbalance that most learning-based methods struggle to cope with. Particularly, traffic sign detection is a challenging problem in which these three issues are seen altogether. To address these challenges, we propose a novel database generation method that requires only (i) arbitrary natural images, i.e., requires no real image from the target-domain, and (ii) templates of the traffic signs. The method does not aim at overcoming the training with real data, but to be a compatible alternative when the real data is not available. The effortlessly generated database is shown to be effective for the training of a deep detector on traffic signs from multiple countries. On large data sets, training with a fully synthetic data set almost matches the performance of training with a real one. When compared to training with a smaller data set of real images, training with synthetic images increased the accuracy by 12.25%. The proposed method also improves the performance of the detector when target-domain data are available.