Abstract：In the detection of camouflaged object, it is difficult to segment camouflaged object accurately due to the high similarity between appearance and backgrounds. In context-aware cross-level fusion network, the high-level semantic information is diluted and lost when it is transmitted to the shallow network fusion, resulting in the reduction of accuracy. Aiming at the problem, an camouflaged object detection(COD) network based on global multi-scale feature fusion(GMF2Net) is proposed. Firstly, the global enhanced fusion module(GEFM) is designed to capture the context information at different scales, and then the high-level semantic information is transmitted to the shallow network through different fusion enhanced branches to reduce the feature loss during multi-scale fusion. The location capture mechanism is designed in the high-level network to extract and refine the location of the camouflaged object, and feature extraction and fusion for high-resolution images are carried out in shallow network to enhance high-resolution feature details. Experiments on three benchmark datasets show that GMF2Net produces better performance.
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2022, Vol. 35 
