Deep Traffic Sign Detection and Recognition Without Target Domain Real Images

• URL: http://arxiv.org/abs/2008.00962v1
• Date: Thu, 30 Jul 2020 21:06:47 GMT
• Title: Deep Traffic Sign Detection and Recognition Without Target Domain Real Images
• Authors: Lucas Tabelini, Rodrigo Berriel, Thiago M. Paix\~ao, Alberto F. De Souza, Claudine Badue, Nicu Sebe and Thiago Oliveira-Santos
• Abstract summary: We propose a novel database generation method that 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. On large data sets, training with a fully synthetic data set almost matches the performance of training with a real one.
• Score: 52.079665469286496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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