COD10K-C: Benchmarking Robustness of Camouflaged Object Detection Under Natural Image Corruptions
arXiv:2606.02603v1 Announce Type: cross Abstract: Camouflaged object detection has improved substantially, but most standard benchmarks evaluate models only on clean images. This is not realistic because real cameras often capture blur, sensor noise, weather effects, and compression artifacts. We present COD10K-C, a corruption robustness benchmark based on COD10K. It includes 8 corruption types and 5 severity levels, giving 40 conditions and 81,040 evaluation pairs in total. We evaluate three popular camouflaged object detection models, SINet-v2, PFNet, and ZoomNet, as well as a lightweight mo
This benchmark is being developed now as camouflaged object detection models mature and the limitations of current evaluation methods become apparent in real-world applications.
It highlights the critical need for AI models to perform robustly in diverse, imperfect real-world conditions, moving beyond idealized benchmark scenarios.
The introduction of COD10K-C shifts model evaluation towards emphasizing robustness against natural image corruptions, potentially influencing future model design and training methodologies.
- · AI robustness researchers
- · Computer vision developers
- · Surveillance technology providers
- · Models vulnerable to image corruption
- · Purely academic benchmarks
Camouflaged object detection models will be evaluated more rigorously for real-world applicability.
Increased focus on data augmentation and corruption-robust model architectures will emerge in the AI community.
Improved practical deployment of object detection systems in harsh or dynamic environments, leading to higher reliability in defense or environmental monitoring.
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Read at arXiv cs.LG