Image classifiers often rely on convolutional neural networks (CNN) for their tasks, which are inherently more heavyweight than multilayer perceptrons (MLPs), which can be problematic in real-time applications. Additionally, many image classification models work on both RGB and grayscale datasets. Classifiers that operate solely on grayscale images are much less common. Grayscale image classification has diverse applications, including but not limited to medical image classification and synthetic aperture radar (SAR) automatic target recognition (ATR). Thus, we present a novel grayscale (single channel) image classification approach using a vectorized view of images. We exploit the lightweightness of MLPs by viewing images as a vector and reducing our problem setting to the grayscale image classification setting. We find that using a single graph convolutional layer batch-wise increases accuracy and reduces variance in the performance of our model. Moreover, we develop a customized accelerator on FPGA for the proposed model with several optimizations to improve its performance. Our experimental results on benchmark grayscale image datasets demonstrate the effectiveness of the proposed model, achieving vastly lower latency (up to 16$\times$ less) and competitive or leading performance compared to other state-of-the-art image classification models on various domain-specific grayscale image classification datasets.

翻译：图像分类器通常依赖卷积神经网络（CNN）执行任务，其本质上比多层感知机（MLP）更重，这可能在实时应用中造成问题。此外，许多图像分类模型同时适用于RGB和灰度数据集，而专门处理灰度图像的分类器则相对少见。灰度图像分类具有广泛的应用，包括但不限于医学图像分类和合成孔径雷达（SAR）自动目标识别（ATR）。为此，我们提出一种基于图像向量化视图的新型灰度（单通道）图像分类方法。通过将图像视为向量并将问题限定于灰度图像分类场景，我们充分利用了MLP的轻量特性。研究发现，逐批次使用单图卷积层能提高模型准确率并降低性能方差。此外，我们为所提模型在FPGA上设计了定制化加速器，并采用多项优化措施提升其性能。在基准灰度图像数据集上的实验结果表明，该模型在多个特定领域的灰度图像分类数据集中，相比其他先进图像分类模型实现了显著更低的延迟（最高降低16倍）以及具有竞争力或领先的性能表现。