  Insects as pollinators play a crucial role in ecosystem management and world
food production. However, insect populations are declining, calling for
efficient methods of insect monitoring. Existing methods analyze video or
time-lapse images of insects in nature, but the analysis is challenging since
insects are small objects in complex and dynamic scenes of natural vegetation.
In this work, we provide a dataset of primary honeybees visiting three
different plant species during two months of the summer period. The dataset
consists of 107,387 annotated time-lapse images from multiple cameras,
including 9,423 annotated insects. We present a method pipeline for detecting
insects in time-lapse RGB images. The pipeline consists of a two-step process.
Firstly, the time-lapse RGB images are preprocessed to enhance insects in the
images. This Motion-Informed-Enhancement technique uses motion and colors to
enhance insects in images. Secondly, the enhanced images are subsequently fed
into a Convolutional Neural network (CNN) object detector. The method improves
the deep learning object detectors You Only Look Once (YOLO) and Faster
Region-based CNN (Faster R-CNN). Using Motion-Informed-Enhancement, the
YOLO-detector improves the average micro F1-score from 0.49 to 0.71, and the
Faster R-CNN-detector improves the average micro F1-score from 0.32 to 0.56 on
the dataset. Our dataset and proposed method provide a step forward to automate
the time-lapse camera monitoring of flying insects. The dataset is published
on: https://vision.eng.au.dk/mie/
