Enhancing autonomous driving safety: a robust traffic sign detection and recognition model TSD-YOLO
As autonomous driving technology rapidly advances, Traffic Sign Detection and Recognition (TSDR) has become pivotal in ensuring the safety and regulatory compliance of autonomous vehicles. Despite progress, existing technologies struggle under challenging conditions such as adverse weather and compl...
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| Main Authors: | , , , , |
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| Format: | Article |
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Elsevier B.V.
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/113642/ https://linkinghub.elsevier.com/retrieve/pii/S016516842400238X |
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| Summary: | As autonomous driving technology rapidly advances, Traffic Sign Detection and Recognition (TSDR) has become pivotal in ensuring the safety and regulatory compliance of autonomous vehicles. Despite progress, existing technologies struggle under challenging conditions such as adverse weather and complex roadway environments. To overcome these obstacles, we introduce a novel model, TSD-YOLO, which leverages Mamba and YOLO technologies to enhance the accuracy and robustness of traffic sign detection. Our innovative YOLO-MAM dual-branch module merges convolutional layer-based local feature extraction with the long-distance dependency capabilities of the State Space Models (SSMs). We conducted experimental validations using the Tsinghua-Tencent 100K (TT-100K) dataset and the Mapillary Traffic Sign Detection (MTSD) dataset, demonstrating our model's efficacy across various datasets. Furthermore, cross-dataset validations affirm the model's exceptional generalization and robustness across diverse environments. This study not only bolsters traffic sign detection and recognition in autonomous driving systems but also paves the way for future advancements in autonomous driving technology. |
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