Line segment detectors with deformable attention

IF 3.4 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Signal Processing-Image Communication Pub Date : 2024-06-07 DOI:10.1016/j.image.2024.117155
Shoufeng Tang , Shuo Zhou , Xiamin Tong , Jingyuan Gao , Yuhao Wang , Xiaojun Ma
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Abstract

The object detectors based on Transformers are advancing rapidly. On the contrary, the development of line segment detectors is relatively slow. It is noteworthy that the object and line segments are both 2D targets. In this work, we design a line segment detection algorithm based on deformable attention. Leveraging this algorithm and the line segment loss function, we transform the object detectors, Deformable DETR and ViDT, into end-to-end line segment detectors named Deformable LETR and ViDTLE, respectively. In order to adapt the idea of sparse modeling for line segment detection, we propose a new attention mechanism named line segment deformable attention (LSDA). This mechanism focuses on the valuable positions under the guidance of reference line to refine line segments. We design an auxiliary algorithm named line segment iterative refinement for LSDA. With as few modifications as possible, we transform two object detection detectors, namely SMCA DETR and PnP DETR into competitive line segment detectors named SMCA LETR and PnP LETR, respectively. The experimental results show that the performances of the proposed methods are efficient.

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具有可变形注意力的线段检测器
基于变压器的物体检测器发展迅速。相反,线段检测器的发展则相对缓慢。值得注意的是,物体和线段都是二维目标。在这项工作中,我们设计了一种基于可变形注意力的线段检测算法。利用该算法和线段损失函数,我们将物体检测器(Deformable DETR 和 ViDT)转换为端到端线段检测器,分别命名为 Deformable LETR 和 ViDTLE。为了将稀疏建模的思想应用于线段检测,我们提出了一种名为线段可变形关注(LSDA)的新关注机制。该机制关注参考线引导下的有价值位置,以细化线段。我们为 LSDA 设计了一种名为线段迭代细化的辅助算法。通过尽可能少的修改,我们将 SMCA DETR 和 PnP DETR 这两种目标检测器分别转化为 SMCA LETR 和 PnP LETR 这两种具有竞争力的线段检测器。实验结果表明,所提出的方法性能高效。
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来源期刊
Signal Processing-Image Communication
Signal Processing-Image Communication 工程技术-工程:电子与电气
CiteScore
8.40
自引率
2.90%
发文量
138
审稿时长
5.2 months
期刊介绍: Signal Processing: Image Communication is an international journal for the development of the theory and practice of image communication. Its primary objectives are the following: To present a forum for the advancement of theory and practice of image communication. To stimulate cross-fertilization between areas similar in nature which have traditionally been separated, for example, various aspects of visual communications and information systems. To contribute to a rapid information exchange between the industrial and academic environments. The editorial policy and the technical content of the journal are the responsibility of the Editor-in-Chief, the Area Editors and the Advisory Editors. The Journal is self-supporting from subscription income and contains a minimum amount of advertisements. Advertisements are subject to the prior approval of the Editor-in-Chief. The journal welcomes contributions from every country in the world. Signal Processing: Image Communication publishes articles relating to aspects of the design, implementation and use of image communication systems. The journal features original research work, tutorial and review articles, and accounts of practical developments. Subjects of interest include image/video coding, 3D video representations and compression, 3D graphics and animation compression, HDTV and 3DTV systems, video adaptation, video over IP, peer-to-peer video networking, interactive visual communication, multi-user video conferencing, wireless video broadcasting and communication, visual surveillance, 2D and 3D image/video quality measures, pre/post processing, video restoration and super-resolution, multi-camera video analysis, motion analysis, content-based image/video indexing and retrieval, face and gesture processing, video synthesis, 2D and 3D image/video acquisition and display technologies, architectures for image/video processing and communication.
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