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Journal of System Simulation

Abstract

Abstract: Accurate recognition of traffic signs plays an important role in the field of intelligent driving. Traffic sign training datasets with long-tail distribution increase the difficulty of traffic sign recognition. A traffic sign recognition model with long-tail distribution based on YOLOX-Tiny was proposed to improve the poor performance of the model trained on long-tail distribution datasets. A long-tail traffic sign dataset was created based on the TT100K_2021 (tsinghua-tencent 100K 2021) dataset. YOLOX-Tiny was chosen as the underlying model by considering picture numbers in datasets, sample distribution, and model size. Equalization loss v2 (EQL v2) was used as classification loss to balance the head and tail of the classifier, and focal loss(FL) was used as target confidence loss to enhance the model's prediction of target confidence. In order to solve the backpropagation conflicts of feature graphs at different levels on the traditional feature pyramid, enhance the feature reorganization effect, and highlight target feature, up-sampling operator CARAFE, coordinate attention (CA), and CARAFE + adaptively spatial feature fusion modules (CAR-ASFF) were introduced to the neck bidirectional pyramid. The research results show that the improved YOLOX-Tiny model achieves 43.67% and 29.98% respectively in the long-tail traffic sign datasets, namely mAP50 and mAP50:95. The improved model has higher detection accuracy than other target detection models.

First Page

2503

Last Page

2516

CLC

TP391.9

Recommended Citation

Wu Yunpeng, Fu Yingxiong, Shen Lijun, et al. Traffic Sign Recognition Model with Long-Tail Distribution Based on YOLOX-Tiny[J]. Journal of System Simulation, 2024, 36(11): 2503-2516.

Corresponding Author

Cui Feng

DOI

10.16182/j.issn1004731x.joss.23-0906

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