Camouflage Target Segmentation Algorithm Using Multi-Scale Feature Extraction and Multi-Level Attention Mechanism

There are two problems when semantic segmentation algorithm is used in the battlefield environment:The camouflage target segmentation boundary is not ideal,and the small target segmentation accuracy is low.So a camouflage target semantic segmentation algorithm CSS-Net is proposed,which combines multi-scale feature extraction method and multi-level attention mechanism.The algorithm consistes of an encoder-decoder structure.In the encoder part,a lightweight and deep separable convolution with residual structure is used to construct the feature encoder to extract features from the reconnaissance images.In the decoder part,a strategy-selected multi-scale feature fusion module and a multi-level attention feature enhancement module are designed to obtain the multi-scale representation information and the channel information of the images.It furtherly enhances the semantic decoding process while gradually refining the segmentation results.The experiment results show that the CSS-Net algorithm can effectively complete the segmentation and recognition of camouflage targets in complex battlefield environment.The overall mIoU value reaches 91.98%,and the segmentation boundary is improved.Compared with DeepLab v3+algorithm,the mIoU value of CSS-Net on camouflage small targets is increased by 3.71 percents,and the mIoU value on multi-scale targets exceeds 85%.The segmentation effects are improved significantly.