Research on detection and classification of traffic signs with data augmentation
Traffic Sign Detection and Recognition (TSDR) system is an important part of autonomous driver-assistance systems (ADAS), and a hot topic in computer vision research. With the instance segmentation framework proposed, deep learning has entered a new stage. However, the current traffic sign dataset c...
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| Veröffentlicht in: | Multimedia tools and applications 2023-10, Vol.82 (25), p.38875-38899 |
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| creator | Yao, Jiana Chu, Yinze Xiang, Xinjian Huang, Bingqiang Xiaoli, Wu |
| description | Traffic Sign Detection and Recognition (TSDR) system is an important part of autonomous driver-assistance systems (ADAS), and a hot topic in computer vision research. With the instance segmentation framework proposed, deep learning has entered a new stage. However, the current traffic sign dataset can only evaluate the performance of object detection framework. In this paper, a new large-scale ZUST Chinese traffic sign dataset benchmark (ZCTSDB) is created to assess the performance of the object detection and instance segmentation framework. ZCTSDB adopts seven different image amplification strategies to enhance the data, which improves the balance of the traffic sign category in the training concentration. The results showed that the average accuracy of ZCTSDB-augmentation object detection and instance segmentation increased by 1.963% and 1.4218%, respectively, especially for large traffic signs. Mask R-CNN has better detection and anti-interference performance than Faster RCNN. The
mAP
of Mask R-CNN is as high as 74.0580. |
| doi_str_mv | 10.1007/s11042-023-14895-z |
| format | Article |
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mAP
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mAP
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on detection and classification of traffic signs with data augmentation</title><author>Yao, Jiana ; Chu, Yinze ; Xiang, Xinjian ; Huang, Bingqiang ; Xiaoli, Wu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c270t-244934a43cd60e74853a5b467b1338b42dbbfeabca380c435ec4a6e65cf958053</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Advanced driver assistance systems</topic><topic>Classification</topic><topic>Computer Communication Networks</topic><topic>Computer Science</topic><topic>Computer vision</topic><topic>Data augmentation</topic><topic>Data Structures and Information Theory</topic><topic>Datasets</topic><topic>Deep learning</topic><topic>Image enhancement</topic><topic>Image segmentation</topic><topic>Machine learning</topic><topic>Multimedia</topic><topic>Multimedia Information Systems</topic><topic>Neural networks</topic><topic>Object recognition</topic><topic>Performance evaluation</topic><topic>Signs</topic><topic>Special Purpose and Application-Based Systems</topic><topic>Traffic control</topic><topic>Traffic signs</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yao, Jiana</creatorcontrib><creatorcontrib>Chu, Yinze</creatorcontrib><creatorcontrib>Xiang, Xinjian</creatorcontrib><creatorcontrib>Huang, Bingqiang</creatorcontrib><creatorcontrib>Xiaoli, Wu</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma 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With the instance segmentation framework proposed, deep learning has entered a new stage. However, the current traffic sign dataset can only evaluate the performance of object detection framework. In this paper, a new large-scale ZUST Chinese traffic sign dataset benchmark (ZCTSDB) is created to assess the performance of the object detection and instance segmentation framework. ZCTSDB adopts seven different image amplification strategies to enhance the data, which improves the balance of the traffic sign category in the training concentration. The results showed that the average accuracy of ZCTSDB-augmentation object detection and instance segmentation increased by 1.963% and 1.4218%, respectively, especially for large traffic signs. Mask R-CNN has better detection and anti-interference performance than Faster RCNN. The
mAP
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| subjects | Accuracy Advanced driver assistance systems Classification Computer Communication Networks Computer Science Computer vision Data augmentation Data Structures and Information Theory Datasets Deep learning Image enhancement Image segmentation Machine learning Multimedia Multimedia Information Systems Neural networks Object recognition Performance evaluation Signs Special Purpose and Application-Based Systems Traffic control Traffic signs |
| title | Research on detection and classification of traffic signs with data augmentation |
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