Long-Range Dependence Involutional Network for Logo Detection

Autor: Xingzhuo Li, Sujuan Hou, Baisong Zhang, Jing Wang, Weikuan Jia, Yuanjie Zheng
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Entropy, Vol 25, Iss 1, p 174 (2023)
Druh dokumentu: article
ISSN: 25010174
1099-4300
73211605
DOI: 10.3390/e25010174
Popis: Logo detection is one of the crucial branches in computer vision due to various real-world applications, such as automatic logo detection and recognition, intelligent transportation, and trademark infringement detection. Compared with traditional handcrafted-feature-based methods, deep learning-based convolutional neural networks (CNNs) can learn both low-level and high-level image features. Recent decades have witnessed the great feature representation capabilities of deep CNNs and their variants, which have been very good at discovering intricate structures in high-dimensional data and are thereby applicable to many domains including logo detection. However, logo detection remains challenging, as existing detection methods cannot solve well the problems of a multiscale and large aspect ratios. In this paper, we tackle these challenges by developing a novel long-range dependence involutional network (LDI-Net). Specifically, we designed a strategy that combines a new operator and a self-attention mechanism via rethinking the intrinsic principle of convolution called long-range dependence involution (LD involution) to alleviate the detection difficulties caused by large aspect ratios. We also introduce a multilevel representation neural architecture search (MRNAS) to detect multiscale logo objects by constructing a novel multipath topology. In addition, we implemented an adaptive RoI pooling module (ARM) to improve detection efficiency by addressing the problem of logo deformation. Comprehensive experiments on four benchmark logo datasets demonstrate the effectiveness and efficiency of the proposed approach.
Databáze: Directory of Open Access Journals
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