As an essential part of Internet of Things, monocular depth estimation (MDE) predicts dense depth maps from a single red-green-blue (RGB) image captured by monocular cameras. Past MDE methods almost focus on improving accuracy at the cost of increased latency, power consumption, and computational complexity, failing to balance accuracy and efficiency. Additionally, when speeding up depth estimation algorithms, researchers commonly ignore their adaptation to different hardware architectures on edge devices. This article aims to solve these challenges. First, we design an efficient MDE model for precise depth sensing on edge devices. Second, We employ a reinforcement learning algorithm and automatically prune redundant channels of MDE by finding a relatively optimal pruning policy. The pruning approach lowers model runtime and power consumption with little loss of accuracy through achieving a target pruning ratio. Finally, we accelerate the pruned MDE while adapting it to different hardware architectures with a compilation optimization method. The compilation optimization further reduces model runtime by an order of magnitude on hardware architectures. Extensive experiments confirm that our methods are effective for images of different sizes on two public datasets. The pruned and optimized MDE achieves promising depth sensing with a better tradeoff among model runtime, accuracy, computational complexity, and power consumption than the state of the arts on different hardware architectures.

中文翻译：

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物联网边缘设备的高效单目深度估计


作为物联网的重要组成部分，单目深度估计 (MDE) 可根据单目相机捕获的单个红绿蓝 (RGB) 图像预测密集的深度图。过去的MDE方法几乎专注于提高准确性，但代价是增加延迟、功耗和计算复杂性，未能平衡准确性和效率。此外，在加速深度估计算法时，研究人员通常会忽略它们对边缘设备上不同硬件架构的适应。本文旨在解决这些挑战。首先，我们设计了一个高效的 MDE 模型，用于边缘设备上的精确深度传感。其次，我们采用强化学习算法，通过寻找相对最优的剪枝策略来自动剪枝 MDE 的冗余通道。该剪枝方法通过实现目标剪枝率，降低了模型运行时间和功耗，并且精度损失很小。最后，我们通过编译优化方法加速剪枝后的MDE，同时使其适应不同的硬件架构。编译优化进一步将硬件架构上的模型运行时间减少了一个数量级。大量的实验证实我们的方法对于两个公共数据集上不同尺寸的图像是有效的。经过修剪和优化的 MDE 实现了有前景的深度传感，与不同硬件架构上的现有技术相比，在模型运行时间、准确性、计算复杂性和功耗之间取得了更好的权衡。