MTTSNet: Military time-sensitive targets stealth network via real-time mask generation

Abstract

The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications. We propose a novel Military Time-sensitive Targets Stealth Network via Real-time Mask Generation (MTTSNet). According to our knowledge, this is the first technology to automatically remove military targets in real-time from videos. The critical steps of MTTSNet are as follows: First, we designed a real-time mask generation network based on the encoder-decoder framework, combined with the domain expansion structure, to effectively extract mask images. Specifically, the ASPP structure in the encoder could achieve advanced semantic feature fusion. The decoder stacked high-dimensional information with low-dimensional information to obtain an effective mask layer. Subsequently, the domain expansion module guided the adaptive expansion of mask images. Second, a context adversarial generation network based on gated convolution was constructed to achieve background restoration of mask positions in the original image. In addition, our method worked in an end-to-end manner. A particular semantic segmentation dataset for military time-sensitive targets has been constructed, called the Military Time-sensitive Target Masking Dataset (MTMD). The MTMD dataset experiment successfully demonstrated that this method could create a mask that completely occludes the target and that the target could be hidden in real time using this mask. We demonstrated the concealment performance of our proposed method by comparing it to a number of well-known and highly optimized baselines.

Keywords

• Deep learning
• Military application
• Targets stealth network
• Mask generation
• Generative adversarial network