View in EDS HTML Full Text PDF Full Text

Research on Polar Environment Target Detection and Intelligent Recognition System Based on Lightweight YOLO Dual-Path Optimization.

Saved in:
Bibliographic Details
Title: Research on Polar Environment Target Detection and Intelligent Recognition System Based on Lightweight YOLO Dual-Path Optimization.
Authors: Jian, Jun1,2 (AUTHOR), Guo, Jiawei1,2 (AUTHOR) tianshui@dlmu.edu.cn
Source: Remote Sensing. May2026, Vol. 18 Issue 10, p1498. 23p.
Subjects: Object recognition (Computer vision), Image processing, Optimization algorithms, Data augmentation, Computer vision, Signal detection, Ice navigation, Polar climate
Geographic Terms: Arctic regions
Abstract: Highlights: What are the main findings? A high-quality polar dataset with 1342 images was established, and a dual-path optimized lightweight YOLO model incorporating reflection suppression and feature enhancement was proposed. The optimized YOLOv8n obtained 0.858 mAP@0.5 and 84.3% practical detection accuracy on shipborne equipment in polar environments. What are the implications of the main findings? The lightweight and high-precision detection model is suitable for real-time target recognition on resource-constrained shipborne platforms. The developed visual intelligent system with three-level risk assessment provides effective technical support for intelligent and safe polar navigation. With the melting of Arctic sea ice and extended navigable windows, polar navigation has gained prominent commercial and strategic value but faces challenges like strong ice reflection, high target texture similarity, and large obstacle scale variation. Aiming at scarce polar-specific datasets, poor adaptability of general algorithms, and disconnection between identification and navigation decisions, this study constructed a technical system integrating "dataset construction–algorithm improvement–system development". A purpose-built polar dataset with 1342 images (covering drift ice, iceberg, ice channel, and ship) was built via web crawling, video frame extraction, and data augmentation. A dual-path optimization scheme for lightweight YOLO models was proposed: the YUV + CLAHE module suppresses strong reflection, and the IceTextureAttention module enhances discriminability of similar targets, with SCConv optimizing computational efficiency. A visual intelligent system embedded with a Polar Code-based risk assessment module was developed to output three-level risks and navigation suggestions. Experimental results show the optimized YOLOv8n + YUV + CLAHE model achieves an overall mAP@0.5 of 0.858 and a recall rate of 0.821. The system runs stably on shipborne equipment with an average image processing latency of 85 ms and a practical detection accuracy of 84.3%, effectively reducing crew workload and improving polar navigation safety. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 194141023
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Research on Polar Environment Target Detection and Intelligent Recognition System Based on Lightweight YOLO Dual-Path Optimization.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Jian%2C+Jun%22">Jian, Jun</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guo%2C+Jiawei%22">Guo, Jiawei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> tianshui@dlmu.edu.cn</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 10, p1498. 23p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+detection%22">Signal detection</searchLink><br /><searchLink fieldCode="DE" term="%22Ice+navigation%22">Ice navigation</searchLink><br /><searchLink fieldCode="DE" term="%22Polar+climate%22">Polar climate</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Arctic+regions%22">Arctic regions</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? A high-quality polar dataset with 1342 images was established, and a dual-path optimized lightweight YOLO model incorporating reflection suppression and feature enhancement was proposed. The optimized YOLOv8n obtained 0.858 mAP@0.5 and 84.3% practical detection accuracy on shipborne equipment in polar environments. What are the implications of the main findings? The lightweight and high-precision detection model is suitable for real-time target recognition on resource-constrained shipborne platforms. The developed visual intelligent system with three-level risk assessment provides effective technical support for intelligent and safe polar navigation. With the melting of Arctic sea ice and extended navigable windows, polar navigation has gained prominent commercial and strategic value but faces challenges like strong ice reflection, high target texture similarity, and large obstacle scale variation. Aiming at scarce polar-specific datasets, poor adaptability of general algorithms, and disconnection between identification and navigation decisions, this study constructed a technical system integrating "dataset construction–algorithm improvement–system development". A purpose-built polar dataset with 1342 images (covering drift ice, iceberg, ice channel, and ship) was built via web crawling, video frame extraction, and data augmentation. A dual-path optimization scheme for lightweight YOLO models was proposed: the YUV + CLAHE module suppresses strong reflection, and the IceTextureAttention module enhances discriminability of similar targets, with SCConv optimizing computational efficiency. A visual intelligent system embedded with a Polar Code-based risk assessment module was developed to output three-level risks and navigation suggestions. Experimental results show the optimized YOLOv8n + YUV + CLAHE model achieves an overall mAP@0.5 of 0.858 and a recall rate of 0.821. The system runs stably on shipborne equipment with an average image processing latency of 85 ms and a practical detection accuracy of 84.3%, effectively reducing crew workload and improving polar navigation safety. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=194141023
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/rs18101498
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 23
        StartPage: 1498
    Subjects:
      – SubjectFull: Object recognition (Computer vision)
        Type: general
      – SubjectFull: Image processing
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
      – SubjectFull: Data augmentation
        Type: general
      – SubjectFull: Computer vision
        Type: general
      – SubjectFull: Signal detection
        Type: general
      – SubjectFull: Ice navigation
        Type: general
      – SubjectFull: Polar climate
        Type: general
      – SubjectFull: Arctic regions
        Type: general
    Titles:
      – TitleFull: Research on Polar Environment Target Detection and Intelligent Recognition System Based on Lightweight YOLO Dual-Path Optimization.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Jian, Jun
      – PersonEntity:
          Name:
            NameFull: Guo, Jiawei
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 05
              Text: May2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 20724292
          Numbering:
            – Type: volume
              Value: 18
            – Type: issue
              Value: 10
          Titles:
            – TitleFull: Remote Sensing
              Type: main
ResultId 1