基于机器学习的军服搜救识别

Benjamin Dubetsky, Kevin Fernandez, Garrett Christopher, Lakhan Singh, Jason Hughes, Jeremy Cole, M. Novitzky
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引用次数: 0

摘要

目标分类是一个迅速发展的主题,在民用和军事专业中都被证明具有许多用途。随着该领域的不断发展,无人驾驶飞机等技术可以执行过去不可能完成的任务,陆军及其部队可以以更安全、更高效的方式完成任务。该项目的目的是开发一种分类器模型,可以在搜索和救援(SAR)任务中自主识别和跟踪人员。这项技术的实施将潜在地提高效率,降低陆军SAR任务的风险,允许士兵派出不必要的机器人,而不是冒着不可或缺的生命危险。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Military Uniform Identification for Search And Rescue (SAR) through Machine Learning
Object classification is a rapidly growing topic that is proving to serve many uses in both civilian and military professions. With continued development in this field, the Army and its units can accomplish tasks in more safe and efficient manners as unmanned drones and other technologies can carry out missions that have not been possible in the past. The purpose of this project is to develop a classifier model that can autonomously identify and track personnel during search and rescue (SAR) missions. The implementation of this technology would potentially improve the efficiency and reduce the risk of SAR missions in the Army by allowing soldiers to send out dispensable robots instead of risking indispensable lives.
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