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This paper presents an in-depth study on the impact of high-quality, comprehensive annotations on camouflaged object detection (COD) performance. We evaluate 13 state-of-the-art COD models trained on original annotations versus a re-annotated version created under stricter, more consistent guidelines using the Cotton Bollworm dataset. Experimental results demonstrate that enhanced annotation quality significantly improves both Intersection over Union (IoU) scores and instance recall, reducing undetected camouflaged objects by an average of 4.6% in Structure-measure and 7.0% in weighted F-measure. The re-annotation process identified 1.4% additional instances, with an average area refinement of 6.3%, primarily through improvements in boundary precision and the detection of previously missed instances. These findings underscore the crucial role of precise annotations in advancing COD performance and validate the data-centric AI paradigm, suggesting that systematic refinement of annotations should be prioritized in computer vision pipelines. The re-annotated dataset is available on GitHub https://cod-espol.github.io/ReannotatedCottonBollworm/ .

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The Hidden Cost of Poor Annotations: How Label Quality Affects Camouflaged Object Detection Performance

  • 会议论文

  • 发表日期：

    2026年

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  • Heny O. Velesaca,
  • Andrea Mero,
  • Anjali Singh,
  • Angel Sappa

摘要

This paper presents an in-depth study on the impact of high-quality, comprehensive annotations on camouflaged object detection (COD) performance. We evaluate 13 state-of-the-art COD models trained on original annotations versus a re-annotated version created under stricter, more consistent guidelines using the Cotton Bollworm dataset. Experimental results demonstrate that enhanced annotation quality significantly improves both Intersection over Union (IoU) scores and instance recall, reducing undetected camouflaged objects by an average of 4.6% in Structure-measure and 7.0% in weighted F-measure. The re-annotation process identified 1.4% additional instances, with an average area refinement of 6.3%, primarily through improvements in boundary precision and the detection of previously missed instances. These findings underscore the crucial role of precise annotations in advancing COD performance and validate the data-centric AI paradigm, suggesting that systematic refinement of annotations should be prioritized in computer vision pipelines. The re-annotated dataset is available on GitHub https://cod-espol.github.io/ReannotatedCottonBollworm/ .