Dayeon Kim — accepted AI-conference papers

2019

Collision Detection for Industrial Collaborative Robots: A Deep Learning Approach

Young Jin Heo, Dayeon Kim, Woongyong Lee, Hyoungkyun Kim, Jonghoon Park, Wan Kyun Chung

RA-L 2019

With increased human-robot interactions in industrial settings, a safe and reliable collision detection framework has become an indispensable element of collaborative robots. The conventional framework detects collisions by estimating collision monitoring signals with a particular type of observer,

Cited by 157 Source ↗Scholar ↗

2018

Super-High-Purity Seed Sorter Using Low-Latency Image-Recognition Based on Deep Learning

Young Jin Heo, Se Jin Kim, Dayeon Kim, Keondo Lee, Wan Kyun Chung

RA-L 2018

Most commercial optical sorting systems are designed to achieve high throughput, so they use a naive low-latency image processing for object identification. These naive low-latency algorithms have difficulty in accurately identifying objects with various shapes, textures, sizes, and colors, so the p

Cited by 49 Source ↗Scholar ↗