Camouflaged Object Detection using Multi-Level Feature Cross-Fusion

Camouflaged object detection (COD) aims to segment objects that closely resemble their surroundings. Accurately recognizing camouflaged objects in these complex environments is challenging due to factors such as low illumination, object occlusion, small size, and similar background. To this end, we propose a novel network for camouflaged object detection, the Multi-Level Feature Cross-Fusion Network (MFCF-Net). This framework aims to learn and utilize background features at different scales through cross-fusion, thereby improving detection accuracy. The core of our approach is to use a modified version of the Pyramid Vision Transformer (PVTv2) as a backbone network to effectively capture contextual information at different scales. Then, we design the Multi-scale Feature Enhancement (MFE) module to optimize features at each scale. In addition, to enhance the model’s ability to recognize camouflaged objects in complex contexts, we cross-fused these enhanced features. Finally, we designed the Balanced Multilevel Feature Cross-Fusion (BMFCF) module. This module improves the accuracy of camouflaged object detection by deeply learning and effectively utilizing contextual feature information and cross-fusing these multi-scale features. Extensive research results show that our MFCF-Net significantly outperforms 18 leading methods on four widely used standard datasets.