Deep traffic sign detection and recognition without target domain real images
| Autor: | Lucas Tabelini, Rodrigo Berriel, Thiago M. Paixão, Alberto F. De Souza, Claudine Badue, Nicu Sebe, Thiago Oliveira-Santos |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Traffic sign detection · Deep learning · Autonomous driving · Object detection Hardware and Architecture Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Computer Vision and Pattern Recognition Software Computer Science Applications |
| Popis: | 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. Comment: arXiv admin note: text overlap with arXiv:1907.09679 |
| Databáze: | OpenAIRE |
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