Insect Identification in Pulsed Lidar Images Using Changepoint Detection Algorithms

Nathaniel Sweeney, Caroline Xu, Joseph A. Shaw, Toby D. Hocking, Bradley M. Whitaker

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Noninvasive entomological insect monitoring often utilizes a variety of tools such as LiDAR to gather information without interfering with the insects in their habitat. These collection methods often result in large amounts of data that can be te-dious and lengthy to interpret and analyze. Machine learning has been previously used in the past in order to analyze Li-DAR images to detect insects, but often suffers from pitfalls such as long training times and large computational power requirements. In an attempt to offer an alternative that takes little to no training on the data and much less computational power, this paper looks at the use of changepoint detection algorithms to analyze LiDAR images containing insects. By analyzing the rows or columns of a LiDAR image, the algorithms should be able to detect abrupt changes in the image that would represent the insects. While not as accurate, the changepoint detection algorithms give comparable results to a machine learning algorithm tested on the same dataset without the need for supervised training.

Original languageEnglish (US)
Title of host publication2023 Intermountain Engineering, Technology and Computing, IETC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages93-97
Number of pages5
ISBN (Electronic)9798350335903
DOIs
StatePublished - 2023
Event2023 Annual Intermountain Engineering, Technology and Computing, IETC 2023 - Provo, United States
Duration: May 12 2023May 13 2023

Publication series

Name2023 Intermountain Engineering, Technology and Computing, IETC 2023

Conference

Conference2023 Annual Intermountain Engineering, Technology and Computing, IETC 2023
Country/TerritoryUnited States
CityProvo
Period5/12/235/13/23

Keywords

  • Anomaly Detection
  • Changepoint Analysis
  • LiDAR

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Instrumentation
  • Education
  • Artificial Intelligence
  • Computer Science Applications
  • Software
  • Electrical and Electronic Engineering

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