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Open AccessArticle

Fast Depth Estimation in a Single Image Using Lightweight Efficient Neural Network

Department of Computer Engineering, Keimyung University, Daegu 42601, Korea
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Sensors 2019, 19(20), 4434; https://doi.org/10.3390/s19204434
Received: 30 September 2019 / Revised: 9 October 2019 / Accepted: 12 October 2019 / Published: 13 October 2019
(This article belongs to the Special Issue Intelligent Sensor Signal in Machine Learning)
Depth estimation is a crucial and fundamental problem in the computer vision field. Conventional methods re-construct scenes using feature points extracted from multiple images; however, these approaches require multiple images and thus are not easily implemented in various real-time applications. Moreover, the special equipment required by hardware-based approaches using 3D sensors is expensive. Therefore, software-based methods for estimating depth from a single image using machine learning or deep learning are emerging as new alternatives. In this paper, we propose an algorithm that generates a depth map in real time using a single image and an optimized lightweight efficient neural network (L-ENet) algorithm instead of physical equipment, such as an infrared sensor or multi-view camera. Because depth values have a continuous nature and can produce locally ambiguous results, pixel-wise prediction with ordinal depth range classification was applied in this study. In addition, in our method various convolution techniques are applied to extract a dense feature map, and the number of parameters is greatly reduced by reducing the network layer. By using the proposed L-ENet algorithm, an accurate depth map can be generated from a single image quickly and, in a comparison with the ground truth, we can produce depth values closer to those of the ground truth with small errors. Experiments confirmed that the proposed L-ENet can achieve a significantly improved estimation performance over the state-of-the-art algorithms in depth estimation based on a single image. View Full-Text
Keywords: depth estimation; convolutional neural network; lightweight efficient neural network; model; single image; ordinal regression depth estimation; convolutional neural network; lightweight efficient neural network; model; single image; ordinal regression
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Kim, S.; Nam, J.; Ko, B. Fast Depth Estimation in a Single Image Using Lightweight Efficient Neural Network. Sensors 2019, 19, 4434.

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