COD10K-C: Benchmarking Robustness of Camouflaged Object Detection Under Natural Image Corruptions
Researchers have introduced COD10K-C, a new benchmark designed to test the robustness of camouflaged object detection models against various image corruptions. The benchmark includes 8 types of corruptions across 5 severity levels, totaling 40 conditions and over 81,000 evaluation pairs. When tested, popular models like SINet-v2 and PFNet showed significant performance degradation, particularly with motion and Gaussian blur, while a new model, RobustCODLite, demonstrated superior resilience through corruption augmentation and specialized architectural components. AI
IMPACT This benchmark will drive development of more resilient computer vision models for real-world applications.