Long-Tailed Traffic Sign Detection Using Attentive Fusion and Hierarchical Group Softmax

Long-Tailed Traffic Sign Detection Using Attentive Fusion and Hierarchical Group Softmax

Traffic sign detection and recognition (TSDR) has attracted extensive studies recently due to its broad application prospect in Intelligent Transport Systems. TSDR is still challenging due to the small size of traffic signs in the image. Besides, the traffic signs in the real world exhibit a long-ta...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 23; no. 12; pp. 24105 - 24115
Main Authors: Gao, Erfeng, Huang, Weiguo, Shi, Juanjuan, Wang, Xiang, Zheng, Jianying, Du, Guifu, Tao, Yanyun
Format: Journal Article
Language:English
Published: New York IEEE 01-12-2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Categories
Convolution
Feature extraction
Feature maps
Fuses
Image color analysis
Intelligent transportation systems
long-tailed detection
Modules
Object detection
Signs
small object detection
Tail
Traffic control
Traffic sign detection and recognition
Traffic signs
Training
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Summary:Traffic sign detection and recognition (TSDR) has attracted extensive studies recently due to its broad application prospect in Intelligent Transport Systems. TSDR is still challenging due to the small size of traffic signs in the image. Besides, the traffic signs in the real world exhibit a long-tailed distribution (i.e., data for most categories are scarce while for others are abundant.), which will lead to a significant performance drop of the detection framework. In this paper, we propose a novel traffic sign detection framework to address these challenging problems. In order to detect small traffic signs, we propose an effective adaptive and attentive spatial feature fusion module which learns the spatial attention map to fuse different feature maps at each scale while emphasizing or suppressing the features at different regions. This module can significantly alleviate the inconsistency among features and enhance feature representations of small objects. Furthermore, to address the long-tailed data problem, a hierarchical group softmax head which constructs a label tree to divide categories into different groups is proposed, in this way, categories in each group have relatively similar frequencies, then the softmax is applied in each relatively balanced group to calculate the probability of each category. Extensive experiments conducted on the TT100K and GTSDB datasets demonstrate that the proposed method achieves notable improvement in both the small traffic signs and long-tailed detection problems in TSDR.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2022.3200737