K. Bjerge

Towards edge processing of images from insect camera traps

This paper presents an image processing pipeline for analyzing insect camera trap data, enabling detection, tracking, and classification of nocturnal insects, with evaluation of edge computing devices for real-time processing.

A deep learning pipeline for time-lapse camera monitoring of insects and their floral environments

This paper presents a deep learning pipeline to monitor arthropod abundance and floral cover using time-lapse camera data, showing a correlation between flower cover and arthropod activity.

Hierarchical Classification of Insects with Multitask Learning and Anomaly Detection

This paper presents an algorithm for hierarchical insect classification using multitask learning and anomaly detection, improving accuracy at higher taxonomic ranks while identifying classification uncertainty.

Accurate detection and identification of insects from camera trap images with deep learning

This paper presents a large insect image dataset and evaluates YOLO models for accurate detection and identification of insects in camera trap images.

Deep learning and computer vision will transform entomology

This paper discusses how deep learning and computer vision can improve insect monitoring by enabling non-invasive, continuous data collection and analysis of insect abundance, diversity, and phenotypic traits.

An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learning

This paper introduces an automated moth trap with computer vision and deep learning to monitor moths, achieving high image capture and classification performance.