Mr. Brian Rapp Profile

Brian Rapp

at U.S. Army Research Lab

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Proceedings Article | 6 June 2024 Presentation + Paper

Quantization to accelerate inference in multimodal 3D object detection

Billy Geerhart, Venkat Dasari, Brian Rapp, Peng Wang, Ju Wang, Christopher Payne

Proceedings Volume 13058, 1305807 (2024) https://doi.org/10.1117/12.3013702

KEYWORDS: Matrices, LIDAR, Image segmentation, Matrix multiplication, Quantization, Mathematical optimization, Point clouds, Object detection

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The Label-Diffusion-LIDAR-Segmentation (LDLS) algorithm uses multi-modal data for enhanced inference of environmental categories. The algorithm segments the Red-Green-Blue (RGB) channels and maps the results to the LIDAR point cloud using matrix calculations to reduce noise. Recent research has developed custom optimization techniques using quantization to accelerate the 3D object detection using LDLS in robotic systems. These optimizations achieve a 3x speedup over the original algorithm, making it possible to deploy the algorithm in real-world applications. The optimizations include quantization for the segmentation inference as well as matrix optimizations for the label diffusion. We will present our results, compare them with the baseline, and discuss their significance in achieving real-time object detection in resource-constrained environments.

Proceedings Article | 12 April 2021 Presentation + Paper

Binary ensemble machine learning for confident decisions

Brian Rapp, Barry Secrest

Proceedings Volume 11729, 117290D (2021) https://doi.org/10.1117/12.2585771

KEYWORDS: Machine learning, Binary data, Reliability

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Folk wisdom on the subject of human knowledge holds that it is “better to know nothing than to know what ain’t so¹”. In some circumstances that precept may be particularly important. If the negative consequences of false knowledge are sufficiently severe, we may be willing to forgo the benefits of knowing some facts to avoid the dangers of believing “facts” that are incorrect. This is the foundation of the “innocent until proven guilty” system of justice. According to English jurist William Blackstone, “it is better that ten guilty persons escape than that one innocent suffer²”. Similar principles apply wherever it is especially harmful to act upon false beliefs. If we wish to employ machine learning as an aid to human judgment, it may in some cases be advisable to insist upon near-certainty from the machine’s reported results. One way of working towards this goal is to use an ensemble of different agents. If their results are consistent with each other, we can have greater confidence in their overall reliability. We can also set a threshold value for the average confidence of the agents themselves. This paper explores the decision-making process for developing an ensemble of classifiers, and evaluates the results in the context of an example set. This set is appropriately categorized by a hierarchical structure, which permits less-specific judgments to be made if confidence falls below our predetermined threshold. We examine the tradeoffs to be made when setting parameters, and discuss aligning them with overarching requirements.

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