A novel traffic sign recognition approach for open scenarios 面向开放场景的交通标志识别方法
Traffic sign recognition systems based on the traditional deep learning technologies typically follow the complete data-driven mode, resulting in their unstable performances and significant security risks when applied to the real-world open scenarios. To alleviate this problem, a novel method is proposed by constructing the semantic data set based on road traffic sign design standards and using the zero-shot learning (ZSL) mechanism to develop a general TSR framework with reasoning and interpretation capabilities. This method can effectively overcome the problems of dynamic update of road traffic signs and classes missing in practice. Furthermore, the national standard for road traffic signs is used to abstract the general attributes of all classes and then the information is injected into the training process of traditional data-driven model as domain knowledge. With the help of domain knowledge, the proposed ZSL-based TSR method can recognize traffic signs that have not been seen in the training stage more accurately than random prediction and traditional deep learning models. Experimental results on the Chinese traffic sign database (CTSDB) and the German traffic sign recognition benchmark (GTSRB) demonstrate that our method, which trains a semantic auto-encoder model, can significantly improve the accuracy in traditional zero-shot learning settings. Specifically, when identifying previously unseen traffic signs in the training set, our approach achieves an improvement in accuracy of at least 29. 96% and 24. 25% on CTSDB and GTSRB, respectively, compared to random prediction. The study verifies the feasibility and effectiveness of the proposed scheme.
Cited Times [WOS]:0
|Document Type||Journal Article|
1.Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ),Shenzhen,Guangdong Province,518107,China
2.Research Institute of Trustworthy Autonomous System,Southern University of Science and Technology,Shenzhen,Guangdong Province,518055,China
Cao，Weipeng,Wu，Yuhao,Li，Dachuan,等. A novel traffic sign recognition approach for open scenarios 面向开放场景的交通标志识别方法[J]. Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering,2023,40(3):258-265.
Cao，Weipeng,Wu，Yuhao,Li，Dachuan,Ming，Zhong,Chen，Zhenru,&Ye，Xuan.(2023).A novel traffic sign recognition approach for open scenarios 面向开放场景的交通标志识别方法.Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering,40(3),258-265.
Cao，Weipeng,et al."A novel traffic sign recognition approach for open scenarios 面向开放场景的交通标志识别方法".Shenzhen Daxue Xuebao (Ligong Ban)/Journal of Shenzhen University Science and Engineering 40.3(2023):258-265.
|Files in This Item:||There are no files associated with this item.|
|Recommend this item|
|Export to Endnote|
|Export to Excel|
|Export to Csv|
|Similar articles in Google Scholar|
|Similar articles in Baidu Scholar|
|Similar articles in Bing Scholar|
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.