Enhancing Monocular Depth Estimation via Image Pre-processing Techniques

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN) Pub Date : 2022-12-04 DOI:10.1109/CICN56167.2022.10008288

M. Syed, Abdulrahman Javaid, Asaad A. Alduais, M. H. Shullar, U. Baroudi, Mustafa Alnasser

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引用次数: 1

Abstract

Robots and drones are getting popular in many applications nowadays. Autonomous operations of drones and robots are highly desirable to minimize human interventions and enhance operation efficiency. However, there are several challenges that need to be overcome before robots and drones can be automated with minimum hardware requirements. Currently, robotics industry employs costly sensors such as Lidar to estimate distance between a vehicle and objects. Recent advancement in Artificial Intelligence (AI) encouraged researcher to investigate techniques to estimate the distance between vehicle and objects using monocular camera and AI. However, distance (depth) estimation using monocular camera still suffers from low accuracy rate in depth estimation. This paper aims to improve the depth estimation values through applying several image pre-processing techniques such as Nonuniform Illumination Removal, Local Adaptive Thresholding, Histogram Equalization, Adaptive Histogram Equalization, White Balance, and Homo- morphic filtering techniques.

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通过图像预处理技术增强单目深度估计

如今，机器人和无人机在许多应用中越来越受欢迎。为了最大限度地减少人为干预，提高操作效率，无人机和机器人的自主操作是非常可取的。然而，在机器人和无人机能够以最低的硬件要求实现自动化之前，还有几个挑战需要克服。目前，机器人行业采用昂贵的传感器，如激光雷达来估计车辆与物体之间的距离。随着人工智能(AI)的发展，研究人员开始研究利用单目相机和人工智能来估计车辆与物体之间距离的技术。然而，单目相机的距离(深度)估计在深度估计中仍然存在准确率较低的问题。本文旨在通过应用非均匀光照去除、局部自适应阈值分割、直方图均衡化、自适应直方图均衡化、白平衡和同人滤波等图像预处理技术来提高深度估计值。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)

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