Training deep neural networks for wireless sensor networks using loosely and weakly labeled images
Training deep neural networks for wireless sensor networks using loosely and weakly labeled images
复制标题
使用松散和弱标记图像训练无线传感器网络的深度神经网络
DOI：
10.1016/j.neucom.2020.09.040
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发表时间：
2021-02-28
期刊：
影响因子：
6
通讯作者：
Hu，Haigen
中科院分区：
文献类型：
--
作者：
Zhou，Qianwei;Chen，Yuhang;Hu，Haigen
虽然深度学习在过去几年中取得了显著的成功，但很少有关于将深度神经网络应用于无线传感器网络（WSNs）的报告，用于数据，能量，计算资源有限的图像目标识别。在这项工作中，一个成本效益的域泛化（CEDG）算法已被提出来训练一个有效的网络与最小的劳动力需求。CEDG通过自动分配的合成域将网络从公开可用的源域转移到特定于应用程序的目标域。目标域与参数调整隔离，仅用于模型选择和测试。目标域与源域显著不同，因为它具有新的目标类别并且由失焦、低分辨率、低照明、低拍摄角度的低质量图像组成。经过训练的网络每次预测约有7 M（ResNet-20约为41 M）次乘法，这足够小，可以让数字信号处理器芯片在我们的WSN中进行实时计算。在不可见和不平衡的目标域上，类别级平均错误率降低了41.12%。
Although deep learning has achieved remarkable successes over the past years, few reports have been published about applying deep neural networks to Wireless Sensor Networks (WSNs) for image targets recognition where data, energy, computation resources are limited. In this work, a Cost-Effective Domain Generalization (CEDG) algorithm has been proposed to train an efficient network with minimum labor requirements. CEDG transfers networks from a publicly available source domain to an application-specific target domain through an automatically allocated synthetic domain. The target domain is isolated from parameters tuning and used for model selection and testing only. The target domain is significantly different from the source domain because it has new target categories and is consisted of low-quality images that are out of focus, low in resolution, low in illumination, low in photographing angle. The trained network has about 7 M (ResNet-20 is about 41 M) multiplications per prediction that is small enough to allow a digital signal processor chip to do real-time recognitions in our WSN. The category-level averaged error on the unseen and unbalanced target domain has been decreased by 41.12%.