Bjerge et al. (2025)

@article{https://doi.org/10.1002/rse2.70007,
author = {Bjerge, Kim and Karstoft, Henrik and Høye, Toke T.},
title = {Towards edge processing of images from insect camera traps},
journal = {Remote Sensing in Ecology and Conservation},
volume = {11},
number = {5},
pages = {573-589},
keywords = {biodiversity monitoring, camera trap, computer vision, edge processing, insect classification, tracking},
doi = {https://doi.org/10.1002/rse2.70007},
url = {https://zslpublications.onlinelibrary.wiley.com/doi/abs/10.1002/rse2.70007},
eprint = {https://zslpublications.onlinelibrary.wiley.com/doi/pdf/10.1002/rse2.70007},
abstract = {Abstract Insects represent nearly half of all known multicellular species, but knowledge about them lags behind for most vertebrate species. In part for this reason, they are often neglected in biodiversity conservation policies and practice. Computer vision tools, such as insect camera traps, for automated monitoring have the potential to revolutionize insect study and conservation. To further advance insect camera trapping and the analysis of their image data, effective image processing pipelines are needed. In this paper, we present a flexible and fast processing pipeline designed to analyse these recordings by detecting, tracking and classifying nocturnal insects in a broad taxonomy of 15 insect classes and resolution of individual moth species. A classifier with anomaly detection is proposed to filter dark, blurred or partially visible insects that will be uncertain to classify correctly. A simple track-by-detection algorithm is proposed to track classified insects by incorporating feature embeddings, distance and area cost. We evaluated the computational speed and power performance of different edge computing devices (Raspberry Pi's and NVIDIA Jetson Nano) and compared various time-lapse (TL) strategies with tracking. The minimum difference of detections was found for 2-min TL intervals compared to tracking with 0.5 frames per second; however, for insects with fewer than one detection per night, the Pearson correlation decreases. Shifting from tracking to TL monitoring would reduce the number of recorded images and would allow for edge processing of images in real-time on a camera trap with Raspberry Pi. The Jetson Nano is the most energy-efficient solution, capable of real-time tracking at nearly 0.5 fps. Our processing pipeline was applied to more than 5.7 million images recorded at 0.5 frames per second from 12 light camera traps during two full seasons located in diverse habitats, including bogs, heaths and forests. Our results thus show the scalability of insect camera traps.},
year = {2025}
}