High-resolution processing and sigmoid fusion modules for efficient detection of small objects in an embedded system
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, M. | - |
dc.contributor.author | Kim, H. | - |
dc.contributor.author | Sung, J. | - |
dc.contributor.author | Park, C. | - |
dc.contributor.author | Paik, Joonki | - |
dc.date.accessioned | 2023-02-15T02:41:48Z | - |
dc.date.available | 2023-02-15T02:41:48Z | - |
dc.date.issued | 2023-01 | - |
dc.identifier.issn | 2045-2322 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/60578 | - |
dc.description.abstract | Recent advances in deep learning realized accurate, robust detection of various types of objects including pedestrians on the road, defect regions in the manufacturing process, human organs in medical images, and dangerous materials passing through the airport checkpoint. Specifically, small object detection implemented as an embedded system is gaining increasing attention for autonomous vehicles, drone reconnaissance, and microscopic imagery. In this paper, we present a light-weight small object detection model using two plug-in modules: (1) high-resolution processing module (HRPM) and (2) sigmoid fusion module (SFM). The HRPM efficiently learns multi-scale features of small objects using a significantly reduced computational cost, and the SFM alleviates mis-classification errors due to spatial noise by adjusting weights on the lost small object information. Combination of HRPM and SFM significantly improved the detection accuracy with a low amount of computation. Compared with the original YOLOX-s model, the proposed model takes a two-times higher-resolution input image for higher mean average precision (mAP) using 57% model parameters and 71% computation in Gflops. The proposed model was tested using real drone reconnaissance images, and provided significant improvement in detecting small vehicles. © 2023, The Author(s). | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Nature Research | - |
dc.title | High-resolution processing and sigmoid fusion modules for efficient detection of small objects in an embedded system | - |
dc.type | Article | - |
dc.identifier.doi | 10.1038/s41598-022-27189-5 | - |
dc.identifier.bibliographicCitation | Scientific Reports, v.13, no.1 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.wosid | 000932732000042 | - |
dc.identifier.scopusid | 2-s2.0-85145645329 | - |
dc.citation.number | 1 | - |
dc.citation.title | Scientific Reports | - |
dc.citation.volume | 13 | - |
dc.type.docType | Article | - |
dc.publisher.location | 영국 | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
84, Heukseok-ro, Dongjak-gu, Seoul, Republic of Korea (06974)02-820-6194
COPYRIGHT 2019 Chung-Ang University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.