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首页> 外文期刊>Bulletin of the Polish Academy of Sciences. Technical Sciences >Fast multispectral deep fusion networks
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Fast multispectral deep fusion networks

机译：快速多光谱深融网络

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Most current state-of-the-art computer vision algorithms use images captured by cameras,which operate in the visible spectral range as input data.Thus,image recognition systems that build on top of those algorithms can not provide acceptable recognition quality in poor lighting conditions,e.g.during nighttime.Another significant limitation of such systems is high demand for computational resources,which makes them impossible to use on low-powered embedded systems without GPU support.This work attempts to create an algorithm for pattern recognition that will consolidate data from visible and infrared spectral ranges and allow near real-time performance on embedded systems with infrared and visible sensors.First,we analyze existing methods of combining data from different spectral ranges for object detection task.Based on the analysis,an architecture of a deep convolutional neural network is proposed for the fusion of multi-spectral data.This architecture is based on the single shot multi-box detection algorithm.Comparison analysis of the proposed architecture with previously proposed solutions for the multi-spectral object detection task shows comparable or better detection accuracy with previous algorithms and significant improvement of the running time on embedded systems.This study was conducted in collaboration with Philips Lighting Research Lab and solutions based on the proposed architecture will be used in image recognition systems for the next generation of intelligent lighting systems.Thus,the main scientific outcomes of this work include an algorithm for multi-spectral pattern recognition based on convolutional neural networks,as well as a modification of detection algorithms for working on embedded systems.
机译：大多数最先进的计算机视觉算法都使用摄像机捕捉的图像作为输入数据，这些图像在可见光谱范围内工作。因此，建立在这些算法之上的图像识别系统无法在恶劣的照明条件下（例如夜间）提供可接受的识别质量。这种系统的另一个显著限制是对计算资源的高需求，这使得它们无法在没有GPU支持的低功耗嵌入式系统上使用。这项工作试图创建一种模式识别算法，该算法将整合来自可见光和红外光谱范围的数据，并在带有红外和可见光传感器的嵌入式系统上实现近实时性能。首先，我们分析了用于目标检测任务的组合不同光谱范围数据的现有方法。在此基础上，提出了一种用于多光谱数据融合的深度卷积神经网络结构。该体系结构基于单镜头多盒检测算法。将所提出的体系结构与之前针对多光谱目标检测任务提出的解决方案进行比较分析，结果表明，该体系结构的检测精度与之前的算法相当或更好，并且显著提高了嵌入式系统的运行时间。这项研究是与飞利浦照明研究实验室合作进行的，基于拟议架构的解决方案将用于下一代智能照明系统的图像识别系统。因此，这项工作的主要科学成果包括基于卷积神经网络的多光谱模式识别算法，以及嵌入式系统检测算法的改进。

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