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

07/23/2019
by   Lucas Tabelini Torres, et al.
6

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 average. In addition, the proposed method is able to detect traffic signs with an average precision, recall and F1-score of about 94 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.

READ FULL TEXT

page 1

page 2

page 3

page 4

page 5

page 6

research
07/30/2020

Deep Traffic Sign Detection and Recognition Without Target Domain Real Images

Deep learning has been successfully applied to several problems related ...
research
11/07/2020

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

Deep neural networks come as an effective solution to many problems asso...
research
04/27/2018

Localized Traffic Sign Detection with Multi-scale Deconvolution Networks

Autonomous driving is becoming a future practical lifestyle greatly driv...
research
11/15/2019

Automated Augmentation with Reinforcement Learning and GANs for Robust Identification of Traffic Signs using Front Camera Images

Traffic sign identification using camera images from vehicles plays a cr...
research
09/05/2018

Conditional Transfer with Dense Residual Attention: Synthesizing traffic signs from street-view imagery

Object detection and classification of traffic signs in street-view imag...
research
03/23/2015

A novel pLSA based Traffic Signs Classification System

In this work we developed a novel and fast traffic sign recognition syst...

Please sign up or login with your details

Forgot password? Click here to reset