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Insect Collision Detection Using Machine Learning with Correlation to Climatic Conditions

Research output: Chapter in Book/Report/Conference proceeding › Other chapter contribution › peer-review

Standard Standard

Insect Collision Detection Using Machine Learning with Correlation to Climatic Conditions. / David, Tudur; Jones, Matt; Cross, Paul et al.
202022 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS). IEEE, 2022. (IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)).

Research output: Chapter in Book/Report/Conference proceeding › Other chapter contribution › peer-review

HarvardHarvard

David, T, Jones, M, Cross, P & Palego, C 2022, Insect Collision Detection Using Machine Learning with Correlation to Climatic Conditions. in 202022 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS). IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), IEEE. https://doi.org/10.1109/EAIS51927.2022.9787692

APA

David, T., Jones, M., Cross, P., & Palego, C. (2022). Insect Collision Detection Using Machine Learning with Correlation to Climatic Conditions. In 202022 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS) (IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)). IEEE. https://doi.org/10.1109/EAIS51927.2022.9787692

CBE

David T, Jones M, Cross P, Palego C. 2022. Insect Collision Detection Using Machine Learning with Correlation to Climatic Conditions. In 202022 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS). IEEE. (IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)). https://doi.org/10.1109/EAIS51927.2022.9787692

MLA

David, Tudur et al. "Insect Collision Detection Using Machine Learning with Correlation to Climatic Conditions". 202022 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS). IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS). IEEE. 2022. https://doi.org/10.1109/EAIS51927.2022.9787692

VancouverVancouver

David T, Jones M, Cross P, Palego C. Insect Collision Detection Using Machine Learning with Correlation to Climatic Conditions. In 202022 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS). IEEE. 2022. (IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)). doi: 10.1109/EAIS51927.2022.9787692

Author

David, Tudur ; Jones, Matt ; Cross, Paul et al. / Insect Collision Detection Using Machine Learning with Correlation to Climatic Conditions. 202022 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS). IEEE, 2022. (IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)).

RIS

TY - CHAP

T1 - Insect Collision Detection Using Machine Learning with Correlation to Climatic Conditions

AU - David, Tudur

AU - Jones, Matt

AU - Cross, Paul

AU - Palego, Cristiano

PY - 2022/6/6

Y1 - 2022/6/6

N2 - Insects have experienced dramatic declines over the past 20 years. This has driven the need to monitor populations in relation to the impact of weather conditions and mitigations to conserve insect communities. A machine learning approach is introduced to detect insect collisions on car number plates by considering the differences between number plate images at the start and end of a journey. This is subsequently correlated with various weather conditions and journey parameters to monitor changing insect populations. The present approach does not impose stringent requirements on the resolution/format of plate images, nor involves application of physical frames for image collection.

AB - Insects have experienced dramatic declines over the past 20 years. This has driven the need to monitor populations in relation to the impact of weather conditions and mitigations to conserve insect communities. A machine learning approach is introduced to detect insect collisions on car number plates by considering the differences between number plate images at the start and end of a journey. This is subsequently correlated with various weather conditions and journey parameters to monitor changing insect populations. The present approach does not impose stringent requirements on the resolution/format of plate images, nor involves application of physical frames for image collection.

KW - image analysis

KW - insect monitoring

KW - machine learning

KW - number plate recognition

KW - pattern recognition

U2 - 10.1109/EAIS51927.2022.9787692

DO - 10.1109/EAIS51927.2022.9787692

M3 - Other chapter contribution

T3 - IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)

BT - 202022 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)

PB - IEEE

ER -