3D-Plotting Algorithm for Insects using YOLOv5
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Abstract (arXiv)
In ecological research, accurately collecting spatiotemporal position data is a fundamental task for understanding the behavior and ecology of insects and other organisms. In recent years, advancements in computer vision techniques have reached a stage of maturity where they can support, and in some cases, replace manual observation. In this study, a simple and inexpensive method for monitoring insects in three dimensions (3D) was developed so that their behavior could be observed automatically in experimental environments. The main achievements of this study have been to create a 3D monitoring algorithm using inexpensive cameras and other equipment to design an adjusting algorithm for depth error, and to validate how our plotting algorithm is quantitatively precise, all of which had not been realized in conventional studies. By offering detailed 3D visualizations of insects, the plotting algorithm aids researchers in more effectively comprehending how insects interact within their environments.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 21 references that are in the map. First posted on arXiv 31 Jan 2024.
Builds on
5 of 21 references.
References · 5
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2018YOLOv3: An Incremental Improvementcited by 2,659
- 2018Object Detection with Deep Learning: A Reviewcited by 528
- 2018Deep Learning for Generic Object Detection: A Surveycited by 392
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