Peter Christiansen

ORCID: 0000-0003-1854-587X
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About
Contact & Profiles
Research Areas
  • Smart Agriculture and AI
  • Robotics and Sensor-Based Localization
  • Food Supply Chain Traceability
  • Remote Sensing and LiDAR Applications
  • Leaf Properties and Growth Measurement
  • Wildlife-Road Interactions and Conservation
  • Power Systems and Technologies
  • Advanced Vision and Imaging
  • Species Distribution and Climate Change
  • Industrial Vision Systems and Defect Detection
  • Hydraulic Fracturing and Reservoir Analysis
  • Gaze Tracking and Assistive Technology
  • Offshore Engineering and Technologies
  • UAV Applications and Optimization
  • Advanced Image and Video Retrieval Techniques
  • Multimodal Machine Learning Applications
  • Autonomous Vehicle Technology and Safety
  • Forest Biomass Utilization and Management
  • Robotic Path Planning Algorithms
  • Reservoir Engineering and Simulation Methods
  • Advanced Neural Network Applications
  • Optical measurement and interference techniques
  • Agriculture and Farm Safety
  • Date Palm Research Studies
  • Seismic Imaging and Inversion Techniques

Aarhus University
2014-2019

Bielefeld University
2017

Signal Processing (United States)
2014-2015

In agricultural mowing operations, thousands of animals are injured or killed each year, due to the increased working widths and speeds machinery. Detection recognition wildlife within fields is important reduce mortality and, thereby, promote wildlife-friendly farming. The work presented in this paper contributes automated detection classification thermal imaging. methods results based on top-view images taken manually from a lift motivate towards unmanned aerial vehicle-based recognition....

10.3390/s140813778 article EN cc-by Sensors 2014-07-30

It is hard to create consistent ground truth data for interest points in natural images, since are define clearly and consistently a human annotator. This makes point detectors non-trivial build. In this work, we introduce an unsupervised deep learning-based detector descriptor. Using self-supervised approach, utilize siamese network novel loss function that enables scores positions be learned automatically. The resulting descriptor UnsuperPoint. We use regression of 1) make UnsuperPoint...

10.48550/arxiv.1907.04011 preprint EN other-oa arXiv (Cornell University) 2019-01-01

In this paper, an algorithm for obstacle detection in agricultural fields is presented. The based on existing deep convolutional neural net, which fine-tuned of a specific obstacle. ISO/DIS 18497, emerging standard safety highly automated machinery agriculture, barrel-shaped defined as the should be robustly detected to comply with standard. We show that our net capable detecting precision 99 . 9 % row crops and 90 8 grass mowing, while simultaneously not people other very distinct obstacles...

10.3390/jimaging2010006 article EN cc-by Journal of Imaging 2016-02-15

This paper describes the Windfarm Main Controller and Remote Control System in Horns Rev Offshore Windfarm. It begins with a brief description of windfarm proceeds to discuss conditions for connecting farm grid. The different control modes shows some recorded measurements. also presents remote system. Because size location windfarm, it was necessary introduce main concept as conventional power plants, so output maintenance.

10.1260/030952403322770959 article EN Wind Engineering 2003-09-01

In this paper, we present a multi-modal dataset for obstacle detection in agriculture. The comprises approximately 2 h of raw sensor data from tractor-mounted system grass mowing scenario Denmark, October 2016. Sensing modalities include stereo camera, thermal web 360 ∘ LiDAR and radar, while precise localization is available fused IMU GNSS. Both static moving obstacles are present, including humans, mannequin dolls, rocks, barrels, buildings, vehicles vegetation. All have ground truth...

10.3390/s17112579 article EN cc-by Sensors 2017-11-09

Abstract Information on which weed species are present within agricultural fields is a prerequisite when using robots for site‐specific management. This study proposes method of improving robustness in shape‐based classifying seedlings toward natural shape variations each plant species. To do so, leaves separated from plants and classified individually together with the classification whole plant. The based common, rotation‐invariant features. Based previous classifications plants,...

10.1002/rob.21734 article EN Journal of Field Robotics 2017-06-27

Today, agricultural vehicles are available that can automatically perform tasks such as weed detection and spraying, mowing, sowing while being steered automatically. However, for systems to be fully autonomous self-driven, not only their specific must automated. An accurate robust perception system detecting avoiding all obstacles also realized ensure safety of humans, animals, other surroundings. In this paper, we present a multi-modal obstacle environment recognition approach process...

10.3389/frobt.2018.00028 article EN cc-by Frontiers in Robotics and AI 2018-03-27

In recent years, the drive of Industry 4.0 initiative has enriched industrial and scientific approaches to build self-driving cars or smart factories. Agricultural applications benefit from both advances, as they are in reality mobile driving factories which process environment. Therefore, acurate perception surrounding is a crucial task it involves goods be processed, contrast standard indoor production lines. Environmental processing requires accurate robust quantification order correctly...

10.48550/arxiv.1805.08595 preprint EN other-oa arXiv (Cornell University) 2018-01-01

<ns3:p>Invasive plant species pose ecological threats to native ecosystems, particularly in areas adjacent roadways, considering that roadways represent lengthy corridors through which invasive can propagate. Traditional manual survey methods for monitoring plants are labor-intensive and limited coverage. This paper introduces a high-speed camera system, named CamAlien, designed be mounted on vehicles efficient along roadways. The system captures high-quality images at rapid intervals,...

10.12688/f1000research.141992.2 preprint EN cc-by F1000Research 2024-10-04

Invasive plant species pose ecological threats to native ecosystems, particularly in areas adjacent roadways, considering that roadways represent lengthy corridors through which invasive can propagate. Traditional manual survey methods for monitoring plants are labor-intensive and limited coverage. This paper introduces a high-speed camera system, named CamAlien, designed be mounted on vehicles efficient along roadways. The system captures high-quality images at rapid intervals, monitor the...

10.12688/f1000research.141992.1 preprint EN cc-by F1000Research 2024-04-23

Autonomous driving in agriculture can be eased and more safe if guided by dense depth maps, since maps outlines scene geometry. RGB monocular image has only naive information about although LiDAR accurate information, it provide sparse maps. By interpolating with aligned color image, reliable created.

10.1145/3387168.3387230 article EN Proceedings of the 3rd International Conference on Vision, Image and Signal Processing 2019-08-26

The link between seismic data and subsurface properties suffers from an intrinsic ambiguity, i.e., that many reservoir models fit the same within noise. In some pathological cases, this may cause biases in interpretation of structure earth used exploration management. Inversion techniques for large sets encountered oil industry are well established assumed to be reliable. Although is generally true, thanks integrated knowledge geology other geophysical data, there is, still a significant...

10.3997/2214-4609.201701690 article EN Proceedings 2017-05-26

Denmark&apos;s first major offshore windfarm, Horns Rev, is in operation. The author describes its construction. Danish power company Elsam constructed and owns the site. site sandy to a great depth perfect for pile driving. contracted main suppliers, with MT-Hojgaard providing foundations; Vestas Scandinavian Wind Technology turbines; Nexans cables; Cotas SCADA system; Siemens Enmaco electrical systems. Construction work began spring 2002. Two container ships were adapted erect turbines....

10.1049/pe:20030105 article EN Power Engineering Journal 2003-01-01
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