Trinity‐Yolo: High‐precision logo detection in the real world

Logo detection has a wide range of applications in the multimedia field, such as video advertising research, brand awareness monitoring and analysis, trademark infringement detection, autonomous driving and intelligent transportation. Compared with other types of images, logo images in the real worl...

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Bibliographic Details
Published in:IET image processing 2023-05, Vol.17 (7), p.2272-2283
Main Authors: Mao, KeJi, Jin, RunHui, Chen, KaiYan, Mao, JiaFa, Dai, GuangLin
Format: Article
Language:English
Subjects:
attention mechanism
logo detection
object detection
stripe pooling
weighted boxes fusion
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Summary:Logo detection has a wide range of applications in the multimedia field, such as video advertising research, brand awareness monitoring and analysis, trademark infringement detection, autonomous driving and intelligent transportation. Compared with other types of images, logo images in the real world have greater diversity in appearance and more complex backgrounds. Therefore, identifying logos from images is a challenge. A strong baseline method Trinity‐Yolo, is proposed, which incorporates attention mechanism, stripe pooling and weighted boxes fusion (WBF) into the state‐of‐the‐art Yolov4 framework for large‐scale logo detection. The attention mechanism improves the feature extraction ability of the deep detection model, the stripe pooling expands the field of view of the model and the weighted boxes fusion enables the model to obtain excellent corrections when outputting the prediction boxes. Trinity‐Yolo can solve the problems of lack of training data, multi‐scale objects and inconsistent bounding‐box regression. On the dataset LogoDet‐3K, the average performance of Trinity‐Yolo is 3% higher than that of Yolov4. Compared with other deep detection models, the performance of Trinity‐Yolo is improved more. The experimental performance on other existing datasets verifies the effectiveness of this method. This paper proposes a new logo detection algorithm based on the characteristics of the logo image. The algorithm has achieved excellent performance on large‐scale logo datasets.
ISSN:1751-9659
1751-9667
DOI:10.1049/ipr2.12791