Abstract
Object detection in the traffic domain has faced growing relevance through the years in developing autonomous driving mechanisms. As with vehicles, pedestrians face a very dynamic context, and identifying relevant objects from a pedestrian perspective presents many challenges. Improving the detection of some objects, such as crosswalks, is very relevant in this regard. This paper presents a technique that applies a computer vision approach to automatically generate datasets for training YOLO-based deep learning algorithms. An initial precision of 0.82 achieved with the generated dataset, which is increased to 0.84 after manually removing incorrect annotations. Results show that our approach leverages the dataset building process by reducing the manual workload needed. The approach could be used for training other object detection models used in traffic scenarios.
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Acknowledgments
This work is supported by project SIMUSAFE, funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No 723386. This research is also supported by LIACC (FCT/UID/CEC/0027/2020). The authors are grateful to the SIMUSAFE Consortium’s members for their valuable comments and fruitful discussions throughout the SIMUSAFE Project.
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Rúbio, T.R.P.M. et al. (2020). A Semi-automatic Object Identification Technique Combining Computer Vision and Deep Learning for the Crosswalk Detection Problem. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12490. Springer, Cham. https://doi.org/10.1007/978-3-030-62365-4_59
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DOI: https://doi.org/10.1007/978-3-030-62365-4_59
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