Wani Pranav, Usmani Kashif, Krishnan Gokul, O'Connor Timothy, Javidi Bahram
Opt Express. 2022 Jan 17;30(2):1205-1218. doi: 10.1364/OE.443657.
Traditionally, long wave infrared imaging has been used in photon starved conditions for object detection and classification. We investigate passive three-dimensional (3D) integral imaging (InIm) in visible spectrum for object classification using deep neural networks in photon-starved conditions and under partial occlusion. We compare the proposed passive 3D InIm operating in the visible domain with that of the long wave infrared sensing in both 2D and 3D imaging cases for object classification in degraded conditions. This comparison is based on average precision, recall, and miss rates. Our experimental results demonstrate that cold and hot object classification using 3D InIm in the visible spectrum may outperform both 2D and 3D imaging implemented in long wave infrared spectrum for photon-starved and partially occluded scenes. While these experiments are not comprehensive, they demonstrate the potential of 3D InIm in the visible spectrum for low light applications. Imaging in the visible spectrum provides higher spatial resolution, more compact optics, and lower cost hardware compared with long wave infrared imaging. In addition, higher spatial resolution obtained in the visible spectrum can improve object classification accuracy. Our experimental results provide a proof of concept for implementing visible spectrum imaging in place of the traditional LWIR spectrum imaging for certain object recognition tasks.
传统上，长波红外成像已被用于光子匮乏条件下的目标检测和分类。我们研究了在可见光光谱中利用深度神经网络，在光子匮乏条件和部分遮挡情况下进行目标分类的被动三维（3D）积分成像（InIm）。我们将在可见光域运行的被动3D InIm与长波红外传感在二维和三维成像情况下进行比较，以用于退化条件下的目标分类。这种比较基于平均精度、召回率和误报率。我们的实验结果表明，在可见光光谱中使用3D InIm进行冷热目标分类，在光子匮乏和部分遮挡的场景中可能优于长波红外光谱中的二维和三维成像。虽然这些实验并不全面，但它们证明了可见光光谱中3D InIm在低光应用中的潜力。与长波红外成像相比，在可见光光谱中成像具有更高的空间分辨率、更紧凑的光学器件和更低成本的硬件。此外，在可见光光谱中获得的更高空间分辨率可以提高目标分类的准确性。我们的实验结果为在某些目标识别任务中用可见光光谱成像取代传统的长波红外光谱成像提供了概念验证。
