Automated insect recognition in unlabeled lidar field measurements

Hannah B. Madsen; Trevor C. Vannoy; Elizabeth M. Rehbein; Riley D. Logan; Joseph A. Shaw; Bradley M. Whitaker

doi:10.1117/12.2618660

3 June 2022 Automated insect recognition in unlabeled lidar field measurements

Hannah B. Madsen, Trevor C. Vannoy, Elizabeth M. Rehbein, Riley D. Logan, Joseph A. Shaw, Bradley M. Whitaker

Proceedings Volume 12110, Laser Radar Technology and Applications XXVII; 1211007 (2022) https://doi.org/10.1117/12.2618660
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

In recent years, lidar-based remote sensing has been used for detecting and classifying flying insects, which is based upon the fact that oscillating wings produce a modulated return signal; oscillations from other objects, such as helicopters or drones, might also be detected in a similar manner. Several groups have successfully used machine learning to classify insects in laboratory settings, but data processing in field studies is still performed manually. Compared to laboratory studies, field studies pose additional challenges, such as non-stationary background clutter and high class imbalance. The models we used for detection and classification were the common boosting algorithm AdaBoost, a hybrid sampling/boosting algorithm RUSBoost, and a neural network with a single hidden layer. Previously, we found that the best performances came from the neural network and AdaBoost. In this paper, we test the machine learning models that have been trained on field data collected from Hyalite Creek on other unlabeled field data; in doing so, we demonstrate each model’s ability to detect insects in data from new, unseen environments. We Use labels created by a domain expert to manually check how many of the predicted images actually contained insects.

Conference Presentation

Citation Download Citation

Hannah B. Madsen, Trevor C. Vannoy, Elizabeth M. Rehbein, Riley D. Logan, Joseph A. Shaw, and Bradley M. Whitaker "Automated insect recognition in unlabeled lidar field measurements", Proc. SPIE 12110, Laser Radar Technology and Applications XXVII, 1211007 (3 June 2022); https://doi.org/10.1117/12.2618660

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