中图分类号：TP183
文献标识码：A
DOI：10.20044/j.csdg.2097-1788.2022.03.013
引用格式： 杨辉，权冀川，梁新宇，等. 融合注意力机制的弱监督迷彩伪装目标检测算法[J].网络安全与数据治理，2022，41(3)：81-91.

Weakly supervised camouflage object detection algorithm fused with attention mechanism

Abstract：With the development of computer hardware and artificial intelligence technology, strongly supervised object detection algorithms have achieved great results. However, strongly supervised object detection algorithms need to be trained on large-scale datasets with high annotation accuracy. But in some specific fields, the above conditions are too demanding. For example, image datasets of camouflage objects commonly used in military are more difficult to be obtained and labeled than public datasets. Therefore, this paper adopted a weakly supervised algorithm with lower requirements for datasets to detect the camouflage objects in images. Due to the high degree of fusion between the camouflage objects and the image background, the detection accuracy of the original SPOL(Shallow feature-aware Pseudo supervised Object Localization) algorithm was relatively low. The core of this paper was to integrate the attention mechanism into the SPOL algorithm. After added an attention module, the model could pay more attention to the area with camouflage objects. So, the detection accuracy of the camouflage objects was improved.

Key words :object detection；weakly supervised algorithm；attention mechanism；camouflage object