Detection and Recognition Algorithm for 100-Meter Signage in Urban Rail Transit Based on Connected Domain Segmentation and SVM
Hundred-meter signage within urban rail transit systems offer important benchmark information for train mileage statistics and aid in mapping and positioning. The detection and recognition of these markers contribute to the intelligent operation and maintenance of rail transit vehicles. This study i...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Control and Information Technology
2024-08-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2024.04.011 |
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| Summary: | Hundred-meter signage within urban rail transit systems offer important benchmark information for train mileage statistics and aid in mapping and positioning. The detection and recognition of these markers contribute to the intelligent operation and maintenance of rail transit vehicles. This study identified a general algorithm for detecting and recognizing 100-meter signage, based on traditional techniques in this field. This approach employs a shrinkage algorithm for detection and positioning, and adopts template matching for digit recognition on the 100-meter signage. This general algorithm was then modified and optimized, leading to the development of a 100-meter signage detection and recognition algorithm that utilizes connected domain segmentation for detection and a support vector machine (SVM) for digit recognition. The process begins with image preprocessing, which includes binarization and morphological filling. Following this, noise and patches are removed leveraging connected domain segmentation, to facilitate the localization of the 100-meter signage. The positioned signages are then processed for background noise removal, tilt correction, and character segmentation. Finally, the extracted characters are input into the SVM model for recognition. The optimized algorithm demonstrated significant improvements, with detection accuracies increasing by approximately 34.4 percentage points and recognition accuracies by approximately 25.6 percentage points for 100-meter signage in subsequent experiments. |
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| ISSN: | 2096-5427 |