Image polarity detection opens new vistas in the area of pervasive computing. State-of-the-art frameworks for polarity detection often prove computationally demanding, as they rely on deep learning networks. Thus, one faces major issues when targeting their implementation on resource-constrained embedded devices. This article presents a design strategy for convolutional neural networks that can support image-polarity detection on edge devices. The outcomes of experimental sessions, involving standard benchmarks and a pair of commercial edge devices, confirm the approach suitability.

中文翻译：

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资源受限设备上的图像极性检测

图像极性检测在普适计算领域开辟了新的前景。最先进的极性检测框架通常需要计算，因为它们依赖于深度学习网络。因此，当将它们的实现定位在资源受限的嵌入式设备上时，面临着主要问题。本文提出了一种卷积神经网络的设计策略，可以支持边缘设备上的图像极性检测。涉及标准基准和一对商业边缘设备的实验会议的结果证实了该方法的适用性。