3 citations · 12 across the 5 of their papers we have counts for
5 papers
Developing a Compressed Object Detection Model based on YOLOv4 for Deployment on Embedded GPU Platform of Autonomous System
Issac Sim, Ju-Hyung Lim, Young-Wan Jang +3
Latest CNN-based object detection models are quite accurate but require a high-performance GPU to run in real-time. They still are heavy in terms of memory size and speed for an em…
EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classification
Ce Zhang, Young-Keun Kim, Azim Eskandarian
Classification of EEG-based motor imagery (MI) is a crucial non-invasive application in brain-computer interface (BCI) research. This paper proposes a novel convolutional neural ne…
FRDet: Balanced and Lightweight Object Detector based on Fire-Residual Modules for Embedded Processor of Autonomous Driving
Seontaek Oh, Ji-Hwan You, Young-Keun Kim
For deployment on an embedded processor for autonomous driving, the object detection network should satisfy all of the accuracy, real-time inference, and light model size requireme…
Accurate Alignment Inspection System for Low-resolution Automotive and Mobility LiDAR
Seontake Oh, Ji-Hwan You, Azim Eskandarian +1
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. In this paper, an accu…
Automatic LiDAR Extrinsic Calibration System using Photodetector and Planar Board for Large-scale Applications
Ji-Hwan You, Seon Taek Oh, Jae-Eun Park +2
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-…