关键词: 伪装目标检测, 反向引导, 多尺度卷积, 注意力机制, 特征聚合

Abstract: Since the camouflaged target is highly similar to the background, it is easily confused by background features, making it difficult to distinguish boundary information and extract target features. The current mainstream Camouflaged Object Detection (COD) algorithm mainly studies the camouflage target itself and its boundaries, ignoring the relationship between the image background and the target, and the detection results are not ideal in complex scenes. To this end, in order to explore the potential connection between background and target, an algorithm for camouflaged target detection by mining boundaries and background was proposed. The algorithm consisted of five parts: the encoder processes the initial raw data; the boundary guidance framework extracts more refined boundary features through feature processing and feature mining; the background guidance framework explores more salient features through multi-scale convolution while being based on attention The hybrid attention module is designed to enhance the selection of background features; the information compensation module makes up for the detailed information lost during feature processing; the multi-task collaborative segmentation decoder efficiently fuses the features extracted from different tasks and modules and outputs the final prediction results. Experimental results on three widely used data sets are better than the other 15 state-of-the-art models, especially on CAMO data set, the average absolute error index drops to 0.042.

Key words: camouflaged object detection, reverse guidance, multi-scale convolution, attention mechanism, feature aggregation