Design and Implementation of a Damage Assessment System for Large-Scale Surface Warships Based on Deep Learning

Abstract

Artificial intelligence technology and image recognition technology are playing an increasingly important role in information warfare, while battlefield image recognition and information processing are at the heart of information processing in warfare. This research will use deep learning image recognition technology and QT development platform, combined with target damage tree analysis and Bayesian network inference method, to research and develop the design of large-scale surface warships damage assessment system. A large-scale surface warships damage assessment system was designed. The system can quickly identify the target large-scale surface warships type with an accuracy rate of over 91%. On this basis, damage assessment is carried out in terms of target vulnerability, combatant power analysis, and bullet-eye rendezvous. A new damage classification is established. The system can improve the efficiency of large-scale surface warships damage assessment, can be well combined with the front-line information collection pictures to assess, and overcome the traditional large-scale surface warships damage assessment and problems of slow and inaccurate manual processing of raw data. It provides a new way of thinking for large-scale surface warships damage assessment research.