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Walk the Lines 2: Contour Tracking for Detailed Segmentation

arXiv 2511.05210 ↗First posted 7 Nov 2025cs.CVcited by 0

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Abstract (arXiv)

This paper presents Walk the Lines 2 (WtL2), a unique contour tracking algorithm specifically adapted for detailed segmentation of infrared (IR) ships and various objects in RGB.1 This extends the original Walk the Lines (WtL) [12], which focused solely on detailed ship segmentation in color. These innovative WtLs can replace the standard non-maximum suppression (NMS) by using contour tracking to refine the object contour until a 1-pixel-wide closed shape can be binarized, forming a segmentable area in foreground-background scenarios. WtL2 broadens the application range of WtL beyond its original scope, adapting to IR and expanding to diverse objects within the RGB context. To achieve IR segmentation, we adapt its input, the object contour detector, to IR ships. In addition, the algorithm is enhanced to process a wide range of RGB objects, outperforming the latest generation of contour-based methods when achieving a closed object contour, offering high peak Intersection over Union (IoU) with impressive details. This positions WtL2 as a compelling method for specialized applications that require detailed segmentation or high-quality samples, potentially accelerating progress in several niche areas of image segmentation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 27 references that are in the map. First posted on arXiv 7 Nov 2025.

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14 of 27 references.

References · 14

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2023Segment Anythingcited by 8,158
  3. 2020Image Segmentation Using Deep Learning: A Surveycited by 659
  4. 2023Segment Anything in High Qualitycited by 373
  5. 2019PolarMask: Single Shot Instance Segmentation with Polar Representationcited by 188
  6. 2020Deep Snake for Real-Time Instance Segmentationcited by 111
  7. 2019Fast Interactive Object Annotation with Curve-GCNcited by 95
  8. 2024IRSAM: Advancing Segment Anything Model for Infrared Small Target Detectioncited by 45
  9. 2022E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentationcited by 27
  10. 2019Object Contour and Edge Detection with RefineContourNetcited by 12
  11. 2023Learning to "Segment Anything" in Thermal Infrared Images through Knowledge Distillation with a Large Scale Dataset SATIRcited by 11
  12. 2022MassMIND: Massachusetts Maritime INfrared Datasetcited by 8
2 more
  1. 2024Introducing VaDA: Novel Image Segmentation Model for Maritime Object Segmentation Using New Datasetcited by 2
  2. 2020Walk the Lines: Object Contour Tracing CNN for Contour Completion of Shipscited by 1

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