SIDIA R&D Institute, Manaus 69055-035, Brazil.
Institute of Mathematical and Computer Sciences, University of São Paulo (USP), São Carlos 13566-590, Brazil.
Sensors (Basel). 2021 Nov 12;21(22):7506. doi: 10.3390/s21227506.
Automatic flood detection may be an important component for triggering damage control systems and minimizing the risk of social or economic impacts caused by flooding. Riverside images from regular cameras are a widely available resource that can be used for tackling this problem. Nevertheless, state-of-the-art neural networks, the most suitable approach for this type of computer vision task, are usually resource-consuming, which poses a challenge for deploying these models within low-capability Internet of Things (IoT) devices with unstable internet connections. In this work, we propose a deep neural network (DNN) architecture pruning algorithm capable of finding a pruned version of a given DNN within a user-specified memory footprint. Our results demonstrate that our proposed algorithm can find a pruned DNN model with the specified memory footprint with little to no degradation of its segmentation performance. Finally, we show that our algorithm can be used in a memory-constraint wireless sensor network (WSN) employed to detect flooding events of urban rivers, and the resulting pruned models have competitive results compared with the original models.
自动洪水检测可能是触发灾害控制系统的重要组成部分，可将洪水造成的社会或经济影响风险降至最低。常规摄像机拍摄的河边图像是一种广泛可用的资源，可用于解决此问题。然而，最适合此类计算机视觉任务的最先进的神经网络通常需要大量资源，这对于在具有不稳定互联网连接的低能力物联网 (IoT) 设备中部署这些模型构成了挑战。在这项工作中，我们提出了一种深度神经网络 (DNN) 架构修剪算法，该算法能够在用户指定的内存占用内找到给定 DNN 的修剪版本。我们的结果表明，我们提出的算法可以找到具有指定内存占用的修剪 DNN 模型，而其分割性能几乎没有下降。最后，我们表明，我们的算法可用于检测城市河流洪水事件的内存受限无线传感器网络 (WSN)，并且修剪后的模型与原始模型相比具有竞争力的结果。
