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Real-time traffic sign detection based on multiscale attention and spatial information aggregator

Real-time traffic sign detection based on multiscale attention and spatial information aggregator Traffic sign detection, as an important part of intelligent driving, can effectively guide drivers to regulate driving and reduce the occurrence of traffic accidents. Currently, the deep learning-based detection methods have achieved very good performance. However, existing network models do not adequately consider the importance of lower-layer features for traffic sign detection. The lack of information on the lower-layer features is a major obstacle to the accurate detection of traffic signs. To solve the above problems, we propose a novel and efficient traffic sign detection method. First, we remove a prediction branch of the YOLOv3 network model to reduce the redundancy of the network model parameters and improve the real-time performance of detection. After that, we propose a multiscale attention feature module. This module fuses the feature information from different layers and refines the features to enhance the Feature Pyramid Network. In addition, we introduce a spatial information aggregator. This enables the spatial information of the lower-layer feature maps to be fused into the higher-layer feature maps. The robustness of our proposed method is further demonstrated by experiments on GTSDB, CCTSDB2021 and TT100k datasets. Specifically, the average execution time on CCTSDB2021 demonstrates the excellent real-time performance of our method. The experimental results show that the method has better accuracy than the original YOLOv3 and YOLOv5 network models. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Real-Time Image Processing Springer Journals

Real-time traffic sign detection based on multiscale attention and spatial information aggregator

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References (43)

Publisher
Springer Journals
Copyright
Copyright © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
ISSN
1861-8200
eISSN
1861-8219
DOI
10.1007/s11554-022-01252-w
Publisher site
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Abstract

Traffic sign detection, as an important part of intelligent driving, can effectively guide drivers to regulate driving and reduce the occurrence of traffic accidents. Currently, the deep learning-based detection methods have achieved very good performance. However, existing network models do not adequately consider the importance of lower-layer features for traffic sign detection. The lack of information on the lower-layer features is a major obstacle to the accurate detection of traffic signs. To solve the above problems, we propose a novel and efficient traffic sign detection method. First, we remove a prediction branch of the YOLOv3 network model to reduce the redundancy of the network model parameters and improve the real-time performance of detection. After that, we propose a multiscale attention feature module. This module fuses the feature information from different layers and refines the features to enhance the Feature Pyramid Network. In addition, we introduce a spatial information aggregator. This enables the spatial information of the lower-layer feature maps to be fused into the higher-layer feature maps. The robustness of our proposed method is further demonstrated by experiments on GTSDB, CCTSDB2021 and TT100k datasets. Specifically, the average execution time on CCTSDB2021 demonstrates the excellent real-time performance of our method. The experimental results show that the method has better accuracy than the original YOLOv3 and YOLOv5 network models.

Journal

Journal of Real-Time Image Processing – Springer Journals

Published: Dec 1, 2022

Keywords: Deep learning; Traffic sign detection; YOLO; Small objects; Multiscale attention

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