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Kim Bjerge
Kim Bjerge
Associate Professor, Aarhus University School of Engineering
Verified email at ase.au.dk
Title
Cited by
Cited by
Year
Deep learning and computer vision will transform entomology
TT Høye, J Ärje, K Bjerge, OLP Hansen, A Iosifidis, F Leese, HMR Mann, ...
Proceedings of the National Academy of Sciences 118 (2), e2002545117, 2021
2762021
An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learning
K Bjerge, JB Nielsen, MV Sepstrup, F Helsing-Nielsen, TT Høye
Sensors 21 (2), 343, 2021
832021
Towards the fully automated monitoring of ecological communities
M Besson, J Alison, K Bjerge, TE Gorochowski, TT Høye, T Jucker, ...
Ecology Letters 25 (12), 2753-2775, 2022
792022
A computer vision system to monitor the infestation level of Varroa destructor in a honeybee colony
K Bjerge, CE Frigaard, PH Mikkelsen, TH Nielsen, M Misbih, P Kryger
Computers and Electronics in Agriculture 164, 104898, 2019
672019
Real-time insect tracking and monitoring with computer vision and deep learning
K Bjerge, HMR Mann, TT Høye
Remote Sensing in Ecology and Conservation, 2021
652021
A living laboratory exploring mobile support for everyday life with diabetes
AM Kanstrup, K Bjerge, JE Kristensen
Wireless personal communications 53, 395-408, 2010
402010
Accurate detection and identification of insects from camera trap images with deep learning
K Bjerge, J Alison, M Dyrmann, CE Frigaard, HMR Mann, TT Høye
PLOS Sustainability and Transformation 2 (3), e0000051, 2023
242023
Enhancing non-technical skills by a multidisciplinary engineering summer school
PG Larsen, EL Kristiansen, J Bennedsen, K Bjerge
European Journal of Engineering Education 42 (6), 1076-1096, 2017
182017
A light trap and computer vision system to detect and classify live moths (Lepidoptera) using tracking and deep learning
K Bjerge, JB Nielsen, M Videbæk Sepstrup, F Helsing-Nielsen, TT Høye
bioRxiv, 2020.03. 18.996447, 2020
132020
A scalable and efficient convolutional neural network accelerator using HLS for a system-on-chip design
K Bjerge, JH Schougaard, DE Larsen
Microprocessors and microsystems 87, 104363, 2021
112021
Guide for getting started with SystemC development
K Bjerge
Danish Technological Institute, 2007
112007
Vibration signatures in ball bearings as a function of lubricant viscosity ratio κ, under alternating lubrication conditions
MO Jakobsen, ES Herskind, K Bjerge, P Ahrendt, CF Pedersen, ...
Tribology International 156, 106840, 2021
82021
Camera assisted roadside monitoring for invasive alien plant species using deep learning
M Dyrmann, AK Mortensen, L Linneberg, TT Høye, K Bjerge
Sensors 21 (18), 6126, 2021
72021
Supporting the Partitioning process in Hardware/Software Co-design with VDM-RT
JAE Isasa, PG Larsen, K Bjerge
Nico Plat, Claus Ballegaard Nielsen and Steve Riddle (Eds.), 5, 2012
72012
Accurate image-based identification of macroinvertebrate specimens using deep learning—How much training data is needed?
TT Høye, M Dyrmann, C Kjær, J Nielsen, M Bruus, CL Mielec, ...
PeerJ 10, e13837, 2022
62022
Hierarchical classification of insects with multitask learning and anomaly detection
K Bjerge, Q Geissmann, J Alison, HMR Mann, TT Høye, M Dyrmann, ...
Ecological Informatics 77, 102278, 2023
52023
Object Detection of Small Insects in Time-Lapse Camera Recordings
K Bjerge, CE Frigaard, H Karstoft
Sensors 23 (16), 7242, 2023
52023
A mobile observatory powered by sun and wind for near real time measurements of atmospheric, glacial, terrestrial, limnic and coastal oceanic conditions in remote off-grid areas
S Rysgaard, K Bjerge, W Boone, E Frandsen, M Graversen, TT Høye, ...
HardwareX 12, e00331, 2022
42022
Kamerabaseret overvågning af insekter på grønne bytage
TT Høye, HMR Mann, K Bjerge
Aarhus Universitet, DCE–Nationalt Center for Miljø og Energi©, 2020
32020
3D impurity inspection of cylindrical transparent containers
M Kragh, K Bjerge, P Ahrendt
Measurement Science and Technology 28 (1), 017002, 2016
32016
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