Integrating Deep Embedded Clustering with DeepLabV3 for Effective Camouflaged Object Detection

Camouflaged Object Detection (COD) poses a significant challenge in computer vision due to the minimal contrast between the object and its surroundings. Conventional methods often struggle in such scenarios. This study introduces an approach by integrating DeepLabV3, a cutting-edge semantic segmentation model, with Deep Embedded Clustering (DEC) to enhance COD. From DeepLabV3, the ResNet101 model is fine-tuned to achieve precise segmentation. Then DEC is applied to help with unsupervised feature learning that enables the model to differentiate elusive differences between camouflaged objects and their backgrounds. The proposed method's efficiency is inveterated on the ACOD10K dataset. It shows distinct improvements in quantitative metrics and qualitative evaluations with a Mean Square Error (MSE) of 0.337 compared to other methods. This approach establishes a robust foundation by integrating DEC into deep learning frameworks to tackle the complexities of COD.