mlsanityData to 14 Oct 2026

A machine learning pipeline for automated insect monitoring

arXiv 2406.13031 ↗First posted 18 Jun 2024cs.CVcited by 1

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Abstract (arXiv)

Climate change and other anthropogenic factors have led to a catastrophic decline in insects, endangering both biodiversity and the ecosystem services on which human society depends. Data on insect abundance, however, remains woefully inadequate. Camera traps, conventionally used for monitoring terrestrial vertebrates, are now being modified for insects, especially moths. We describe a complete, open-source machine learning-based software pipeline for automated monitoring of moths via camera traps, including object detection, moth/non-moth classification, fine-grained identification of moth species, and tracking individuals. We believe that our tools, which are already in use across three continents, represent the future of massively scalable data collection in entomology.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 28 references that are in the map. First posted on arXiv 18 Jun 2024.

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9 of 28 references.

References · 9

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  3. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  4. 2023Segment Anythingcited by 8,234
  5. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  6. 2019RandAugment: Practical automated data augmentation with a reduced search spacecited by 2,269
  7. 2018Bag of Tricks for Image Classification with Convolutional Neural Networkscited by 431
  8. 2018Deep Learning for Generic Object Detection: A Surveycited by 392
  9. 2023Deep Learning Pipeline for Automated Visual Moth Monitoring: Insect Localization and Species Classificationcited by 5

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