Abstract
This research focuses on improving traffic sign detection in cars using Convoluted Neural Networks (CNN), with images from the German Traffic Sign database. In order to generate more accurate detection results of traffic signs, different algorithms were used to generate the detection and classification tasks. The Faster Region Based Convolutional Neural Network (Faster R-CNN) and You Only Look Once networks were compared beforehand to determine which CNN to use. The Faster R-CNN was decided upon based off of previous results, then used to generate the classification and detection tasks. Pre-training weights were made using Caffe based off of the German Traffic Sign Recognition Benchmark database. Different methods of generation of training data were then used and compared. The Faster R-CNN network was used to create a classification task based off the images from the self-generated training images, which was tested against the German Traffic Sign Detection Benchmark database.
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Acknowledgments and Institution
我单位的置名格式: Department of Automation and CBICR Center, Tsinghua University; Beijing KLSBDPA Key Laboratory.
论文的资助项目: This work is supported in part by the National Natural Science Foundation of China under Grants 61671266, 61327902, and in part by the Research Project of Tsinghua University under Grant 20161080084, and in part by National High-tech Research and Development Plan under Grant 2015AA042306.
Acknowledgment must be given to the Tsinghua University Department of Automation, for supporting me and allowing me to work with them for this research, and Yan Qi for helping solve issues regarding implementation.
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Peng, E., Chen, F., Song, X. (2017). Traffic Sign Detection with Convolutional Neural Networks. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2016. Communications in Computer and Information Science, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-5230-9_24
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DOI: https://doi.org/10.1007/978-981-10-5230-9_24
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