Search Results for author: YeongMin Ko

Found 4 papers, 1 papers with code

Light Robust Monocular Depth Estimation For Outdoor Environment Via Monochrome And Color Camera Fusion

no code implementations24 Feb 2022 Hyeonsoo Jang, YeongMin Ko, Younkwan Lee, Moongu Jeon

Our methods not only outperform the state-of-the-art works across all metrics but also efficient in terms of cost, memory, and computation.

Autonomous Driving Monocular Depth Estimation +2

Task-Driven Deep Image Enhancement Network for Autonomous Driving in Bad Weather

no code implementations14 Oct 2021 Younkwan Lee, Jihyo Jeon, YeongMin Ko, Byunggwan Jeon, Moongu Jeon

Visual perception in autonomous driving is a crucial part of a vehicle to navigate safely and sustainably in different traffic conditions.

Autonomous Driving Depth Estimation +4

Key Points Estimation and Point Instance Segmentation Approach for Lane Detection

10 code implementations16 Feb 2020 Yeongmin Ko, Younkwan Lee, Shoaib Azam, Farzeen Munir, Moongu Jeon, Witold Pedrycz

In the case of traffic line detection, an essential perception module, many condition should be considered, such as number of traffic lines and computing power of the target system.

Autonomous Driving Clustering +4

Unconstrained Road Marking Recognition with Generative Adversarial Networks

no code implementations10 Oct 2019 Younkwan Lee, Juhyun Lee, Yoojin Hong, YeongMin Ko, Moongu Jeon

Recent road marking recognition has achieved great success in the past few years along with the rapid development of deep learning.

Data Augmentation Deblurring

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