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CB Round Discrimination Fusing Visible and Infrared Camera Data

机译:融合可见光和红外相机数据的CB圆形识别

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In support of the Disparate Sensor Integration (DSI) Program a number of imaging sensors were fielded to determine the feasibility of using information from these systems to discriminate between chemical simulant and high explosives munitions. The imaging systems recorded video from 160 training and 100 blind munitions detonation events. Two types of munitions were used; 155 mm high explosives rounds and 155 mm chemical simulant rounds. In addition two different modes of detonation were used with these two classes of munitions; detonation on impact (point detonation) and detonation prior to impact (airblasts). The imaging sensors fielded included two visible wavelength cameras, a near infrared camera, a mid wavelength infrared camera system and a long wavelength infrared camera system. Our work to date has concentrated on using the data from one of the visible wavelength camera systems and the long wavelength infrared camera system. The results provided in this paper clearly show the potential for discriminating between the two types of munitions and the two detonation modes using these camera data. It is expected that improved classification robustness will be achieved when the camera data described in this paper is combined with results and discriminating features generated from some of the other camera systems as well as the acoustic and seismic sensors also fielded in support of the DSI Program. The paper will provide a brief description of the camera systems and provide still imagery that show the four classes of explosives events at the same point in the munitions detonation sequence in both the visible and long wavelength infrared camera data. Next the methods used to identify frames of interest from the overall video sequence will be described in detail. This will be followed by descriptions of the features that are extracted from the frames of interest. A description of the system that is currently used for performing classification with the extracted features and the results attained on the blind test data set are next described. The work performed to date to fuse information from the visible and long wavelength infrared imaging sensors including the benefits realized are next described. The paper concludes with a description of our ongoing work to fuse imaging sensor data.
机译:为了支持“异构传感器集成”(DSI)计划,已部署了许多成像传感器,以确定使用这些系统中的信息来区分化学模拟弹药和高爆炸药弹药的可行性。成像系统记录了160次训练和100次盲弹爆炸事件的视频。使用了两种弹药。 155毫米高炸药弹和155毫米化学模拟弹。另外,这两类弹药使用了两种不同的爆炸方式。撞击时爆炸(点爆轰)和撞击前爆炸(爆炸)。现场的成像传感器包括两个可见波长相机,一个近红外相机,一个中波长红外相机系统和一个长波长红外相机系统。迄今为止,我们的工作集中在使用来自可见波长摄像头系统和长波长红外摄像头系统之一的数据。本文提供的结果清楚地表明了使用这些相机数据来区分两种类型的弹药和两种爆炸方式的潜力。期望将本文描述的摄像机数据与结果结合起来,并区分其他摄像机系统以及也支持DSI计划的声学和地震传感器产生的特征,从而可以提高分类的鲁棒性。本文将简要介绍摄像头系统,并提供静止图像,以可见光和长波长红外摄像头数据显示弹药爆炸序列中同一点的四类爆炸事件。接下来,将详细描述用于从整个视频序列中识别关注帧的方法。接下来将描述从关注帧中提取的特征。接下来描述当前用于利用提取的特征执行分类的系统的描述以及在盲测数据集上获得的结果。接下来描述迄今为止融合可见光和长波长红外成像传感器信息的工作,包括所实现的好处。本文以对我们正在进行的融合成像传感器数据的工作进行了描述。

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