Towards scalable insect monitoring: Ultra‐lightweight CNNs as on‐device triggers for insect camera traps

Ross J Gardiner, Sareh Rowlands, Benno I Simmons

Abstract:

1. Camera traps, combined with AI, have emerged to achieve automated,
scalable biodiversity monitoring. However, passive infrared (PIR) sensors that
typically trigger camera traps are poorly suited for detecting small, fast-moving
ectotherms such as insects. Insects comprise over half of all animal species and
are key components of ecosystems and agriculture. The need for an appropriate
and scalable insect camera trap is critical in the wake of concerning reports of
declines in insect populations.
2. This study proposes an alternative to the PIR trigger: ultra-lightweight
convolutional neural networks running on low-powered hardware to detect
insects in a continuous stream of captured images. We train such models to
distinguish insect images from backgrounds. Our design achieves zero latency
between trigger and image capture.
3. Our models are rigorously tested and achieve high accuracy ranging from
91.8% to 96.4% AUROC on test data and 58.8% to 87.2% AUROC on field data
from distributions unseen during training. The high specificity of our models
ensures minimal saving of false positive images, maximising deployment storage
efficiency. High recall scores indicate a minimal false negative rate, maximising
insect detection. Analysis using saliency maps shows the learned representation
of our models to be robust, with low reliance on spurious background features.
Our method is also shown to operate deployed on off-the-shelf, low-powered
microcontroller units, consuming a maximum power draw of less than 300 mW.
This paves the way for scalable systems with longer deployment times.
4. Overall, we fully define the properties of a successful trigger for camera traps and
show how lightweight AI models, made bespoke for efficient hardware, can be
realised with a specific focus on insect ectotherms. We provide these models to
the community alongside a complete codebase for future modifications, and we
demonstrate how they can be deployed on an example ESP32-S3 microcontroller
platform. This step potentiates a major advancement for ectotherm camera traps
and insect monitoring.