To develop robust and secure automated transportation systems, Traffic Sign Detection and Recognition (TSDR) is a key part. It plays a crucial role in Advanced Driver Assistance Systems (ADAS), self-driving vehicles and traffic safety. However, the task of TSDR can be challenging due to traffic signs being subject to damages, discoloration, vandalism and occlusion. Even though a lot of progress is made in both research areas of Traffic Sign Detection (TSD) and Traffic Sign Recognition (TSR), no study explicitly deals with the problem of qualitative poor traffic signs appearing in real-world scenarios. This can be assigned to the lack of an extensive traffic sign dataset containing flawless signs as well as imperfect signs. Neural networks trained exclusively on untainted data might fail at detecting flawed signs as they occur in real-world scenarios. Therefore, in this paper, a novel traffic sign dataset with condition annotations is proposed, indicating if a sign is good, discolored, vandalized, dirty or occluded. The custom dataset is created with a semi-supervised approach, in which machine learning models are trained to classify traffic signs in the condition categories. The resulting dataset can be used as basis for more precise traffic sign recognition as well as traffic sign condition classification which can be useful for maintenance planning. The dataset includes approx. 20.000 images of 10 sign classes, where 70% of data is incorporated in the training set, 10% in the validation set and 20% in the test set.
A novel Traffic Sign Dataset with Condition Annotations
05.12.2023
1288080 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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