Motion Informed Object Detection of Small Insects in Time-lapse Camera Recordings

• URL: http://arxiv.org/abs/2212.00423v2
• Date: Thu, 29 Jun 2023 15:01:00 GMT
• Title: Motion Informed Object Detection of Small Insects in Time-lapse Camera Recordings
• Authors: Kim Bjerge, Carsten Eie Frigaard and Henrik Karstoft
• Abstract summary: We present a method pipeline for detecting insects in time-lapse RGB images. Motion-Informed-Enhancement technique uses motion and colors to enhance insects in images. The method improves the deep learning object detectors You Only Look Once (YOLO) and Faster Region-based CNN (Faster R-CNN)
• Score: 1.3965477771846408
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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/

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