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Traffic Sign Detection. Code for the paper entitled "Evaluation of deep neural networks for traffic sign detection systems".
| Date | Stars |
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| 2026-07-24 | 337 |
| 2026-07-25 | 337 |
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# Traffic Sign Detection This is the code for the paper **[Evaluation of deep neural networks for traffic sign detection systems](https://doi.org/10.1016/j.neucom.2018.08.009)** <br> [Álvaro Arcos-García](https://scholar.google.com/citations?user=gjecl3cAAAAJ), [Juan Antonio Álvarez-García](https://scholar.google.com/citations?user=Qk79xk8AAAAJ), [Luis M. Soria-Morillo](https://scholar.google.com/citations?user=poBDpFkAAAAJ) <br> The paper addresses the problem of traffic sign detection analysing the state-of-the-art of several object-detection systems (Faster R-CNN, R-FCN, SSD, and YOLO V2) combined with various feature extractors (Resnet V1 50, Resnet V1 101, Inception V2, Inception Resnet V2, Mobilenet V1, and Darknet-19). We aim to explore the properties of these object-detection models which are modified and specifically adapted to the traffic sign detection problem domain by means of transfer learning. In particular, various publicly available object-detection models that were pre-trained on the Microsoft COCO dataset are fine-tuned on the [German Traffic Sign Detection Benchmark](http://benchmark.ini.rub.de/?section=gtsdb) dataset. The evaluation and comparison of these models include key metrics, such as the mean average precision (mAP), memory allocation, running time, number of floating point operations, number of parameters of the model, and the effect of traffic sign image sizes.  We provide: - Several [pretrained models](#pretrained-models). - [Experiment results](#results). - Test code to [run the model on new images](#running-on-new-images). - Instructions for [training your model](#training). - Instructions for [evaluating your model](#evaluation). - Scripts to create GTSDB TFRecords. If you find this code useful in your research, please cite: ``` "Evaluation of deep neural networks for traffic sign detection systems." Álvaro Arcos-García, Juan A. Álvarez-García, Luis M. Soria-Morillo. Neurocomputing 316 (2018) 332-344. ``` \[[link](https://doi.org/10.1016/j.neucom.2018.08.009)\]\[[bibtex]( https://scholar.googleusercontent.com/citations?view_op=export_citations&user=gjecl3cAAAAJ&citsig=AMstHGQAAAAAW88AbhXW-l4i_qhFLdvy09TVYklB-yqs&hl=en)\] ## Requirements This project is implemented in [Tensorflow](https://www.tensorflow.org/) and it is based on two repositories: [Tensorflow Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection) and [darkflow](https://github.com/thtrieu/darkflow). ## Pretrained models You can download TFRecords of the GTSDB dataset from [Google Drive](https://drive.google.com/open?id=1hKdjTsiFm_vdtZPdto0QJihThGFOPEkq). Unzip it to the `gtsdb_data` folder. You can download pre-trained models from Google Drive. Unzip them to the `models` folder. - [faster_rcnn_inception_resnet_v2_atrous](https://drive.google.com/open?id=12vLvA9wyJ9lRuDl9H9Tls0z5jsX0I0Da) - [faster_rcnn_inception_v2](https://drive.google.com/open?id=1LRCSWIkX_i6ijScMfaxSte_5a_x9tjWF) - [faster_rcnn_resnet_101](https://drive.google.com/open?id=15OxyPlqyOOlUdsbUmdrexKLpHy1l5tP9) - [faster_rcnn_resnet50](https://drive.google.com/open?id=1aEqlozB_CzhyJX_PO6SSiM-Yiv3fuO8V) - [rfcn_resnet101](https://drive.google.com/open?id=1eWCDZ5BxcEa7n_jZmWUr2kwHPBi5-SMG) - [ssd_inception_v2](https://drive.google.com/open?id=1TKMd-wIZJ1aUcOhWburm2b6WgYnP0ZK6) - [ssd_mobilenet_v1](https://drive.google.com/open?id=1U31RhUvE1Urr5Q92AJynMvl-oFBVRxxg) - [yolo_v2](https://drive.google.com/open?id=1wqWgHqcwtjXTXvKZgkzfbTvdpDAy8G85) (Updated 19/02/2019: Add missing file 'yolo_v2-50500.data-00000-of-00001') ## Results ### Overview Our findings show that Faster R-CNN Inception Resnet V2 obtains the best mAP, while R-FCN Resnet 101 strikes the best trade-off between accuracy and execution time. YOLO V2 and SSD Mobilenet merit a special mention, in that the former achieves competitive accuracy results and is the second fastest detector, whi
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matched fp:e1e3b6de665378bc, topic:deep-learning, topic:tensorflow
matched fp:e1e3b6de665378bc, topic:object-detection, readme:object detection