EVALUATION OF A TRAFFIC SIGN DETECTOR BY SYNTHETIC IMAGE DATA FOR ADVANCED DRIVER ASSISTANCE SYSTEMS
Recently, several synthetic image datasets of street scenes have been published. These datasets contain various traffic signs and can therefore be used to train and test machine learning-based traffic sign detectors. In this contribution, selected datasets are compared regarding ther applicability f...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2018-05-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2/425/2018/isprs-archives-XLII-2-425-2018.pdf |
| _version_ | 1811279577178177536 |
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| author | A. Hanel D. Kreuzpaintner U. Stilla |
| author_facet | A. Hanel D. Kreuzpaintner U. Stilla |
| author_sort | A. Hanel |
| collection | DOAJ |
| description | Recently, several synthetic image datasets of street scenes have been published. These datasets contain various traffic signs and can therefore be used to train and test machine learning-based traffic sign detectors. In this contribution, selected datasets are compared regarding ther applicability for traffic sign detection. The comparison covers the process to produce the synthetic images and addresses the virtual worlds, needed to produce the synthetic images, and their environmental conditions. The comparison covers variations in the appearance of traffic signs and the labeling strategies used for the datasets, as well. A deep learning traffic sign detector is trained with multiple training datasets with different ratios between synthetic and real training samples to evaluate the synthetic SYNTHIA dataset. A test of the detector on real samples only has shown that an overall accuracy and ROC AUC of more than 95 % can be achieved for both a small rate of synthetic samples and a large rate of synthetic samples in the training dataset. |
| first_indexed | 2024-04-13T00:56:57Z |
| format | Article |
| id | doaj.art-f15adde2adb94b2d8d3a0a6565179a56 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-04-13T00:56:57Z |
| publishDate | 2018-05-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-f15adde2adb94b2d8d3a0a6565179a562022-12-22T03:09:36ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342018-05-01XLII-242543210.5194/isprs-archives-XLII-2-425-2018EVALUATION OF A TRAFFIC SIGN DETECTOR BY SYNTHETIC IMAGE DATA FOR ADVANCED DRIVER ASSISTANCE SYSTEMSA. Hanel0D. Kreuzpaintner1U. Stilla2Photogrammetry and Remote Sensing, Technical University of Munich, 80333 Munich, GermanyPhotogrammetry and Remote Sensing, Technical University of Munich, 80333 Munich, GermanyPhotogrammetry and Remote Sensing, Technical University of Munich, 80333 Munich, GermanyRecently, several synthetic image datasets of street scenes have been published. These datasets contain various traffic signs and can therefore be used to train and test machine learning-based traffic sign detectors. In this contribution, selected datasets are compared regarding ther applicability for traffic sign detection. The comparison covers the process to produce the synthetic images and addresses the virtual worlds, needed to produce the synthetic images, and their environmental conditions. The comparison covers variations in the appearance of traffic signs and the labeling strategies used for the datasets, as well. A deep learning traffic sign detector is trained with multiple training datasets with different ratios between synthetic and real training samples to evaluate the synthetic SYNTHIA dataset. A test of the detector on real samples only has shown that an overall accuracy and ROC AUC of more than 95 % can be achieved for both a small rate of synthetic samples and a large rate of synthetic samples in the training dataset.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2/425/2018/isprs-archives-XLII-2-425-2018.pdf |
| spellingShingle | A. Hanel D. Kreuzpaintner U. Stilla EVALUATION OF A TRAFFIC SIGN DETECTOR BY SYNTHETIC IMAGE DATA FOR ADVANCED DRIVER ASSISTANCE SYSTEMS The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | EVALUATION OF A TRAFFIC SIGN DETECTOR BY SYNTHETIC IMAGE DATA FOR ADVANCED DRIVER ASSISTANCE SYSTEMS |
| title_full | EVALUATION OF A TRAFFIC SIGN DETECTOR BY SYNTHETIC IMAGE DATA FOR ADVANCED DRIVER ASSISTANCE SYSTEMS |
| title_fullStr | EVALUATION OF A TRAFFIC SIGN DETECTOR BY SYNTHETIC IMAGE DATA FOR ADVANCED DRIVER ASSISTANCE SYSTEMS |
| title_full_unstemmed | EVALUATION OF A TRAFFIC SIGN DETECTOR BY SYNTHETIC IMAGE DATA FOR ADVANCED DRIVER ASSISTANCE SYSTEMS |
| title_short | EVALUATION OF A TRAFFIC SIGN DETECTOR BY SYNTHETIC IMAGE DATA FOR ADVANCED DRIVER ASSISTANCE SYSTEMS |
| title_sort | evaluation of a traffic sign detector by synthetic image data for advanced driver assistance systems |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2/425/2018/isprs-archives-XLII-2-425-2018.pdf |
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