Deep traffic light detection by overlaying synthetic context on arbitrary natural images

Deep neural networks come as an effective solution to many problems associated with autonomous driving. By providing real image samples with traffic context to the network, the model learns to detect and classify elements of interest, such as pedestrians, traffic signs, and traffic lights. However, acquiring and annotating real data can be extremely costly in terms of time and effort. In this context, we propose a method to generate artificial traffic-related training data for deep traffic light detectors. This data is generated using basic non-realistic computer graphics to blend fake traffic scenes on top of arbitrary image backgrounds that are not related to the traffic domain. Thus, a large amount of training data can be generated without annotation efforts. Furthermore, it also tackles the intrinsic data imbalance problem in traffic light datasets, caused mainly by the low amount of samples of the yellow state. Experiments show that it is possible to achieve results comparable to those obtained with real training data from the problem domain, yielding an average mAP and an average F1-score which are each nearly 4 p.p. higher than the respective metrics obtained with a real-world reference model.


page 2

page 4

page 6

page 9

page 11


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

Deep learning has been successfully applied to several problems related ...

Deep Traffic Sign Detection and Recognition Without Target Domain Real Images

Deep learning has been successfully applied to several problems related ...

Unsupervised Traffic Scene Generation with Synthetic 3D Scene Graphs

Image synthesis driven by computer graphics achieved recently a remarkab...

TL-GAN: Improving Traffic Light Recognition via Data Synthesis for Autonomous Driving

Traffic light recognition, as a critical component of the perception mod...

Low-Quality Training Data Only? A Robust Framework for Detecting Encrypted Malicious Network Traffic

Machine learning (ML) is promising in accurately detecting malicious flo...

NoiSER: Noise is All You Need for Enhancing Low-Light Images Without Task-Related Data

This paper is about an extraordinary phenomenon. Suppose we don't use an...

STAN: Synthetic Network Traffic Generation using Autoregressive Neural Models

Deep learning models have achieved great success in recent years. Howeve...

Please sign up or login with your details

Forgot password? Click here to reset