Enhancing camouflaged object detection through contrastive learning and data augmentation techniques

Camouflaged object detection (COD) aims to locate and segment objects that blend into their surroundings, presenting significant challenges due to the high similarity between the objects and their background. This work introduces a novel approach, Contrastive Learning with Augmented Data (CLAD), which enhances COD performance by leveraging contrastive learning and data augmentation. Our method formulates a simplified task by placing camouflaged objects in new environments, creating positive and negative samples for contrast learning. This process strengthens the model's ability to differentiate camouflaged objects from complex backgrounds. Furthermore, we introduce a concatenated feature enhancement module to integrate and enrich multi-scale features, improving the overall expressive power of the model. Extensive experiments on four benchmark datasets demonstrate that CLAD outperforms state-of-the-art COD methods, and its effectiveness extends to salient object detection tasks, achieving competitive results across multiple metrics.