Intelligent Generation Technology for Virtual Target Detection Training Samples Based on ARMA3

As the demand for high-quality training samples in fields such as emergency drills and disaster simulations continues to increase, the use of simulation software to quickly create diverse and highly realistic training data has become an important technical means to address the issue. Based on this, the characteristics and functions of ARMA3 are analyzed, the theoretical basis for generating training samples is discussed, the methods for data acquisition and annotation of ARMA3 training samples are studied, and the intelligent generation technology for virtual target detection training samples that can be quickly generated and applied in multi-domain is proposed. The results of practical case applications show that the technology can significantly improve the efficiency and quality of sample collection, not only providing strong support for emergency rescue and urban safety in the civilian field, but also providing useful references for the generation of training samples in cross-domain simulation software.