Michael Halstead

About Michael Halstead

Michael Halstead, With an exceptional h-index of 11 and a recent h-index of 10 (since 2020), a distinguished researcher at Rheinische Friedrich-Wilhelms-Universität Bonn, specializes in the field of Computer vision, machine learning, robotic vision, surveillance.

His recent articles reflect a diverse array of research interests and contributions to the field:

A cross-domain challenge with panoptic segmentation in agriculture

PAg-NeRF: Towards fast and efficient end-to-end panoptic 3D representations for agricultural robotics

Panoptic one-click segmentation: applied to agricultural data

Bonnbot-i: A precise weed management and crop monitoring platform

Towards autonomous visual navigation in arable fields

Contrastive 3D shape completion and reconstruction for agricultural robots using RGB-D frames

Explicitly incorporating spatial information to recurrent networks for agriculture

Crop agnostic monitoring driven by deep learning

Michael Halstead Information

University

Position

___

Citations(all)

370

Citations(since 2020)

312

Cited By

127

hIndex(all)

11

hIndex(since 2020)

10

i10Index(all)

12

i10Index(since 2020)

11

Email

University Profile Page

Google Scholar

Michael Halstead Skills & Research Interests

Computer vision

machine learning

robotic vision

surveillance

Top articles of Michael Halstead

A cross-domain challenge with panoptic segmentation in agriculture

The International Journal of Robotics Research

2024/1/18

PAg-NeRF: Towards fast and efficient end-to-end panoptic 3D representations for agricultural robotics

IEEE Robotics and Automation Letters

2023/12/1

Panoptic one-click segmentation: applied to agricultural data

IEEE Robotics and Automation Letters

2023/3/8

Bonnbot-i: A precise weed management and crop monitoring platform

2022/10/23

Towards autonomous visual navigation in arable fields

2022/10/23

Contrastive 3D shape completion and reconstruction for agricultural robots using RGB-D frames

IEEE Robotics and Automation Letters

2022/7/22

Explicitly incorporating spatial information to recurrent networks for agriculture

IEEE Robotics and Automation Letters

2022/7/4

Crop agnostic monitoring driven by deep learning

Frontiers in plant science

2021/12/20

Virtual temporal samples for recurrent neural networks: Applied to semantic segmentation in agriculture

2021/9/28

Pathobot: A robot for glasshouse crop phenotyping and intervention

2021/5/30

Fruit Detection in the Wild: The Impact of Varying Conditions and Cultivar

2020

See List of Professors in Michael Halstead University(Rheinische Friedrich-Wilhelms-Universität Bonn)