Shoufeng Tang , Shuo Zhou , Xiamin Tong , Jingyuan Gao , Yuhao Wang , Xiaojun Ma
{"title":"Line segment detectors with deformable attention","authors":"Shoufeng Tang , Shuo Zhou , Xiamin Tong , Jingyuan Gao , Yuhao Wang , Xiaojun Ma","doi":"10.1016/j.image.2024.117155","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":49521,"journal":{"name":"Signal Processing-Image Communication","volume":"128 ","pages":"Article 117155"},"PeriodicalIF":3.4000,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Signal Processing-Image Communication","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0923596524000560","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
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.
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.