An Attention-ReXNet Network for Long Tail Road Scene Classification

With the development of assisted driving and autonomous driving, road scene classification has become increasingly important due to its ability to provide environmental information for drivers and autonomous driving, which is beneficial to reduce traffic accidents. However, road scene classification...

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Bibliographic Details
Published in2023 IEEE 13th International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER) pp. 1104 - 1109
Main Authors Guo, Yuan H., Zhu, Jun R., Yang, Chang C., Yang, B.
Format Conference Proceeding
LanguageEnglish
Published IEEE 11.07.2023
Subjects
Data models
Entropy
Feature extraction
Neural networks
Roads
Scene classification
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Summary:With the development of assisted driving and autonomous driving, road scene classification has become increasingly important due to its ability to provide environmental information for drivers and autonomous driving, which is beneficial to reduce traffic accidents. However, road scene classification is limited by dataset imbalance and running speed, making it difficult to apply practically. To solve the problems mentioned above, we proposed an Attention-ReXNet network to conduct road scene classification. A balance Softmax cross-entropy is introduced to solve the class imbalance problem in road scene classification. The proposed method is evaluated in the Road Surface Classification Dataset (RSCD), which contains 27 categories and 1 million images with class imbalance. Experimental results show that Attention-ReXNet and balance Softmax cross entropy can achieve the best classification performance, with accuracy and top-1 being 87.67% and 88.52%, respectively.
ISSN:2642-6633
DOI:10.1109/CYBER59472.2023.10256482