78 research outputs found
Statistical Models for Improving the Rate of Advance of Buried Target Detection Systems
No full textThe ground penetrating radar (GPR) is one of the most popular and successful sensing modalities that have been investigated for buried target detection (BTD). GPR offers excellent detection performance, however, it is limited by a low rate of advance (ROA) due to its short sensing standoff distance. Standoff distance refers to the distance between the sensing platform and the location in front of the platform where the GPR senses the ground. Large standoff (high ROA) sensing modalities have been investigated as alternatives to the GPR but they do not (yet) achieve comparable detection performance. Another strategy to improve the ROA of the GPR is to combine it with a large standoff sensor within the same BTD system, and to leverage the benefits of the respective modalities. This work investigates both of the aforementioned approaches to improve the ROA of GPR systems using statistical modeling techniques. The first part of the work investigates two large-standoff modalities for BTD systems. New detection algorithms are proposed in both cases with the goal of improving their detection performance so that it is more comparable with the GPR. The second part of the work investigates two methods of combining the GPR with a large standoff modality in order to yield a system with greater ROA, but similar target detection performance. All proposed statistical modeling approaches in this work are tested for efficacy using real field-collected data from BTD systems. The experimental results show that each of the proposed methods contribute towards the goal of improving the ROA of BTD systems.</p
Exploiting Multi-Look Information for Landmine Detection in Forward Looking Infrared Video
No full textForward Looking Infrared (FLIR) cameras have recently been studied as a sensing modality for use in landmine detection systems. FLIR-based detection systems benefit from larger standoff distances and faster rates of advance than other sensing modalities, but they also present significant challenges for detection algorithm design. FLIR video typically yields multiple looks at each object in the scene, each from a different camera perspective. As a result each object in the scene appears in multiple video frames, and each time at a different shape and size. This presents questions about how best to utilize such information. Evidence in the literature suggests such multi-look information can be exploited to improve detection performance but, to date, there has been no controlled investigation of multi-look information in detection. Any results are further confounded because no precise definition exists for what constitutes multi-look information. This thesis addresses these problems by developing a precise mathematical definition of "a look", and how to quantify the multi-look content of video data. Controlled experiments are conducted to assess the impact of multi-look information on FLIR detection using several popular detection algorithms. Based on these results two novel video processing techniques are presented, the plan-view framework and the FLRX algorithm, to better exploit multi-look information. The results show that multi-look information can have a positive or negative impact on detection performance depending on how it is used. The results also show that the novel algorithms presented here are effective techniques for analyzing video and exploiting any multi-look information to improve detection performance.</p
Deep Learning the Properties of Metamaterials
No full textUnlike conventional materials, metamaterials derive their properties primarily from their structure rather than their bulk construction materials. With a carefully-chosen structure, electromagnetic metamaterials have been shown to exhibit exotic properties that are not achievable with conventional materials, and now underpin many technologies. In principle even more exotic and useful properties are achievable, but the modeling and design of advanced metamaterials is challenging, and a major bottleneck to continued progress. In this talk I discuss the challenges of modeling and designing advanced metamaterials, and how recent advances in deep learning – a branch of machine learning - have shown the potential to overcome some of these challenges. In particular, I discuss recent deep learning methods – some developed by myself with collaborators at Duke University - that can dramatically accelerate both the modeling and design of complex metamaterials. In principle these methods can also be applied to many other natural systems, accelerating scientific progress and technological development. I close by discussing some open challenges at the intersection of machine learning and scientific computing
Using queuing models to aid design and guide research effort for multimodality buried target detection systems
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Improving convolutional neural networks for buried target detection in ground penetrating radar using transfer learning via pretraining
No full textA Large Comparison of Feature-Based Approaches for Buried Target Classification in Forward-Looking Ground-Penetrating Radar
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