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

Abstract

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.