Small mammals and bird detection using IoT devices

Kyuwon Shim*, Andre Barczak, Napoleon Reyes, Nasim Ahmed

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

4 Citations (Scopus)


This article describes the application of computer vision to automatically detect pests in protected forests. One of the most threatening pests in NZ are rodents (rats, mice, possums etc). The objective of the application is to allow the deployment of Internet of Things (IoT) devices capable of detecting rodents while differentiating them from birds. As IoT devices have limited memory and CPU capabilities, it is important to find computer vision methods that are efficient and lightweight. Two main methods were employed for this work to test their suitability for this application. The first method is based on getting a profile of moving objects and classify them using Fourier descriptors (FD) and classifiers. The second method uses YOLO (You Only Look Once) to classify birds versus rodents. In order to increase the number of images for training YOLO, a semi-automatic labelling system was created, using the FD method to segment images of birds and rodents. The final accuracy rate using FD reached 83 percent with random forests, and YOLO reached 97 percent. The FD method can outperform YOLO in terms of speed when using a Raspberry Pi, achieving more than 6 frames per second.

Original languageEnglish
Title of host publicationProceedings of the 2021 36th International Conference on Image and Vision Computing New Zealand, IVCNZ 2021
EditorsMichael J. Cree
PublisherIEEE Computer Society
ISBN (Electronic)978-166540645-1
Publication statusPublished - 2021
Externally publishedYes
Event36th International Conference on Image and Vision Computing New Zealand, IVCNZ 2021 - Tauranga, New Zealand
Duration: 9 Dec 202110 Dec 2021
Conference number: 36th

Publication series

NameInternational Conference Image and Vision Computing New Zealand
ISSN (Print)2151-2191
ISSN (Electronic)2151-2205
NameLecture Notes in Computer Science
ISSN (Electronic)1611-3349


Conference36th International Conference on Image and Vision Computing New Zealand, IVCNZ 2021
Abbreviated titleIVCNZ
Country/TerritoryNew Zealand
Internet address


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