AINet: Integrating Mamba and CBAM for Enhanced Camouflage Object Detection

This paper introduces AINet, a novel deep learning architecture designed for detecting camouflaged objects in complex and diverse environments. The objective of this work is to design an end-to-end camouflaged object detection architecture that simultaneously captures long-range dependencies and refines subtle camouflage cues, improving segmentation accuracy and boundary delineation across both standard COD benchmarks and real-world agricultural scenarios. AINet leverages the strengths of Mamba, an efficient sequential state model for capturing long-range dependencies, and the Convolutional Block Attention Module (CBAM) for feature refinement through attention mechanisms. Detecting camouflaged objects is a significant challenge across a wide range of real-world applications, including surveillance, security, medical imaging, and autonomous systems, where objects of interest may blend into their backgrounds and evade conventional detection methods. To demonstrate its effectiveness, AINet is evaluated on multiple datasets, including standard camouflaged object detection benchmarks such as CAMO, COD10K, and NC4K, as well as domain-specific datasets (such as pest and fruit detection). Experimental results show that AINet outperforms existing state-of-the-art models. The implementation is publicly available on GitHub for reproducibility: https://cod-espol.github.io/AINet/.