Insects as pollinators play a key role in ecosystem management and world food
production. However, insect populations are declining, calling for a necessary
global demand 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.
The current paper provides a dataset of primary honeybees visiting three
different plant species during two months of summer-period. The dataset
consists of more than 700,000 time-lapse images from multiple cameras,
including more than 100,000 annotated images. The paper presents a new 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. We propose a new prepossessing enhancement
method: Motion-Informed-enhancement. The technique uses motion and colors to
enhance insects in images. The enhanced images are subsequently fed into a
Convolutional Neural network (CNN) object detector. Motion-Informed-enhancement
improves the deep learning object detectors You Only Look Once (YOLO) and
Faster Region-based Convolutional Neural Networks (Faster R-CNN). Using
Motion-Informed-enhancement the YOLO-detector improves average micro F1-score
from 0.49 to 0.71, and the Faster R-CNN-detector improves average micro
F1-score from 0.32 to 0.56 on the our dataset. Our datasets are published on:
this https URL

本文介绍了一个基于运动信息增量的方法管道，用于在大型、彩色、时间间隔的野外图像数据集中检测昆虫，该方法提高了两个卷积神经网络（YOLO，Faster R-CNN）的检测效率，并发布了一个包含 100,000 个标注昆虫图像的图像数据集。