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Camouflaged object detection by boundary mining and background guidance
Zhonghua LI, Gengxin ZHONG, Ping FAN, Hengliang ZHU
Journal of Computer Applications    2025, 45 (10): 3328-3335.   DOI: 10.11772/j.issn.1001-9081.2024091324
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Since the camouflaged object is highly similar to the background, it is easily confused by background features, making it difficult to distinguish boundary information and extract object features. Current mainstream Camouflaged Object Detection (COD) algorithms mainly study the camouflage object itself and its boundaries, ignoring relationship between the image background and the object, and the detection results are not ideal in complex scenes. To this end, in order to explore potential connection between background and object, an camouflaged object detection algorithm by mining boundaries and background was proposed, called I2DNet (Indirect to Direct Network). The algorithm consists of five parts: in the encoder, the initial raw data was processed; in the Boundary-guided feature Extracting and Mining Framework (BEMF), more refined boundary features were extracted through feature processing and feature mining; in the Latent-feature Exploring Framework based on Background guidance (LEFB), more salient features were explored through multi-scale convolution while based on attention, the Hybrid Attention Module (HAM) was designed to enhance selection of background features; in the Information Supplement Module (ISM), the detailed information lost during feature processing was made up; in the Multi-task Co-segmentation Decoder (MCD), the features extracted from different tasks and modules were fused efficiently and the final prediction results were output. Experimental results show that the proposed algorithm is better than the other 15 state-of-the-art models on three widely used datasets; especially on CAMO dataset, the proposed algorithm has the mean absolute error index dropped to 0.042.

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