YOLOF-F: you only look one-level feature fusion for traffic sign detection

This paper proposes a detector that focuses on multi-scale detection problems and effectively enhances the detection performance to solve the problem that is hard to detect minor traffic signs. This detector, called YOLOF-F (you only look one-level feature fusion), is a single-stage detector that ex...

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Bibliographic Details
Published inThe Visual computer Vol. 40; no. 2; pp. 747 - 760
Main Authors Wei, Hongyang, Zhang, Qianqian, Qin, Yugang, Li, Xiang, Qian, Yurong
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2024
Subjects
Artificial Intelligence
Computer Graphics
Computer Science
Image Processing and Computer Vision
Original Article
Multi-scale detection
Feature fusion
Single-level detector
Traffic sign detection
Corner pooling
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Summary:This paper proposes a detector that focuses on multi-scale detection problems and effectively enhances the detection performance to solve the problem that is hard to detect minor traffic signs. This detector, called YOLOF-F (you only look one-level feature fusion), is a single-stage detector that extracts multi-scale feature information from a single layer of fusion feature. First, we propose FFM (feature fusion module) to fuse different scales. Next, we offer a new encoder CDE (corner dilated encoder) to enhance the angular point information in the feature map, improve position regression accuracy, and maintain a faster detection speed. Finally, YOLOF-F achieved 74.57% and 77.23% of the AP on the GTSDB and CTSD datasets and reached 32 FPS. Extensive experiments validate that YOLOF-F is faster and more effective than most traffic sign detection methods.
ISSN:0178-2789
1432-2315
DOI:10.1007/s00371-023-02813-1