no code implementations • 1 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.
no code implementations • 24 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.
no code implementations • 16 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.
no code implementations • 24 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.
no code implementations • 24 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.