Towards edge processing of images from insect camera traps

Abstract

Insects represent nearly half of all known multicellular species but knowledge about them lacks behind 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 Pis and NVIDIA Jetson Nano) and compared various time-lapse strategies with tracking. The minimum difference was found for 2-minute time-lapse 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 time-lapse monitoring would reduce the amount of recorded images and be able to perform 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.