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Related Experiment Video

Updated: Apr 24, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Spatial Coherence Loss: All Objects Matter in Salient and Camouflaged Object Detection.

Ziyun Yang1, Kevin Choy1, Sina Farsiu1,2,3

  • 1Duke University, Department of Biomedical Engineering, Durham, 27705, NC, USA.

Pattern Recognition
|April 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces Spatial Coherence Loss (SCLoss) to improve object detection by focusing on ambiguous regions. SCLoss enhances salient object detection (SOD) and camouflaged object detection (COD) models by learning object boundaries more effectively.

Keywords:
Camouflaged Object DetectionImage SegmentationPixel RelationshipSalient Object DetectionSpatial Coherence

Related Experiment Videos

Last Updated: Apr 24, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Generic object detection requires accurate modeling of objectness, including both ground truth (GT) and decoy objects.
  • Existing models often overlook decoy objects or use loss functions that only consider single pixels, missing object-level ambiguity information.

Purpose of the Study:

  • To propose a novel loss function, Spatial Coherence Loss (SCLoss), inspired by human visual processing.
  • To improve the learning of ambiguous regions in object detection tasks.

Main Methods:

  • Developed SCLoss, which integrates the mutual response between adjacent pixels into single-response loss functions.
  • Applied SCLoss to state-of-the-art salient object detection (SOD) and camouflaged object detection (COD) models.

Main Results:

  • SCLoss effectively learns ambiguous regions by adaptively detecting and emphasizing their boundaries.
  • Replacing existing loss functions with SCLoss improved the performance of SOTA SOD and COD models.
  • Combining SCLoss with other loss functions further enhanced performance, achieving new SOTA results.

Conclusions:

  • SCLoss offers a significant advancement in modeling objectness, particularly for challenging cases involving ambiguous objects.
  • The proposed method provides a self-adaptive approach to boundary detection, enhancing semantic analysis in object detection.