Search Results for author: Young-Keun Kim

Found 5 papers, 0 papers with code

Developing a Compressed Object Detection Model based on YOLOv4 for Deployment on Embedded GPU Platform of Autonomous System

no code implementations1 Aug 2021 Issac Sim, Ju-Hyung Lim, Young-Wan Jang, JiHwan You, Seontaek Oh, Young-Keun Kim

Since the object detection for autonomous system is run on an embedded processor, it is preferable to compress the detection network as light as possible while preserving the detection accuracy.

Object object-detection +1

EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classification

no code implementations24 Jan 2021 Ce Zhang, Young-Keun Kim, Azim Eskandarian

The proposed CNN model, namely EEG-Inception, is built on the backbone of the Inception-Time network, which showed to be highly efficient and accurate for time-series classification.

Brain Computer Interface Classification +6

FRDet: Balanced and Lightweight Object Detector based on Fire-Residual Modules for Embedded Processor of Autonomous Driving

no code implementations16 Nov 2020 Seontaek Oh, Ji-Hwan You, Young-Keun Kim

The proposed network achieved higher compression with comparable accuracy compared to other deep CNN object detectors while showing improved accuracy than the lightweight detector baselines.

Autonomous Driving Object +2

Automatic LiDAR Extrinsic Calibration System using Photodetector and Planar Board for Large-scale Applications

no code implementations24 Aug 2020 Ji-Hwan You, Seon Taek Oh, Jae-Eun Park, Azim Eskandarian, Young-Keun Kim

This paper presents a novel automatic calibration system to estimate the extrinsic parameters of LiDAR mounted on a mobile platform for sensor misalignment inspection in the large-scale production of highly automated vehicles.

Accurate Alignment Inspection System for Low-resolution Automotive and Mobility LiDAR

no code implementations24 Aug 2020 Seontake Oh, Ji-Hwan You, Azim Eskandarian, Young-Keun Kim

A misalignment of LiDAR as low as a few degrees could cause a significant error in obstacle detection and mapping that could cause safety and quality issues.

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