  Camouflaged Object Detection (COD) is a critical aspect of computer vision
aimed at identifying concealed objects, with applications spanning military,
industrial, medical and monitoring domains. To address the problem of poor
detail segmentation effect, we introduce a novel method for camouflage object
detection, named CoFiNet. Our approach primarily focuses on multi-scale feature
fusion and extraction, with special attention to the model's segmentation
effectiveness for detailed features, enhancing its ability to effectively
detect camouflaged objects. CoFiNet adopts a coarse-to-fine strategy. A
multi-scale feature integration module is laveraged to enhance the model's
capability of fusing context feature. A multi-activation selective kernel
module is leveraged to grant the model the ability to autonomously alter its
receptive field, enabling it to selectively choose an appropriate receptive
field for camouflaged objects of different sizes. During mask generation, we
employ the dual-mask strategy for image segmentation, separating the
reconstruction of coarse and fine masks, which significantly enhances the
model's learning capacity for details. Comprehensive experiments were conducted
on four different datasets, demonstrating that CoFiNet achieves
state-of-the-art performance across all datasets. The experiment results of
CoFiNet underscore its effectiveness in camouflage object detection and
highlight its potential in various practical application scenarios.
