ArticleSensors (Basel, Switzerland)2023
Object Detection of Small Insects in Time-Lapse Camera Recordings.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Machine learning workflows detect ground-dwelling arthropods against heterogenous backgrounds.Journal of insect science (Online) · 2026Article
- Successes and limitations of pretrained YOLO detectors applied to unseen time-lapse images for automated pollinator monitoring.Scientific reports · 2025Article
- A Deep Learning-Based Model Approach for Quantitative Analysis of Cell Chemotaxis in a Microfluidic Chip.Sensors (Basel, Switzerland) · 2025Article
- A novel target-oriented enhanced infrared camera trap data screening method.Scientific reports · 2025Article
- Improving Object Detection for Time-Lapse Imagery Using Temporal Features in Wildlife Monitoring.Sensors (Basel, Switzerland) · 2024Article
- Article
- Detecting common coccinellids found in sorghum using deep learning models.Scientific reports · 2023Article
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Authors and funding
3 authors.
Funding
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Abstract
As pollinators, insects play a crucial role in ecosystem management and world food production. However, insect populations are declining, necessitating efficient insect monitoring methods. Existing methods analyze video or time-lapse images of insects in nature, but analysis is challenging as insects are small objects in complex and dynamic natural vegetation scenes. In this work, we provide a dataset of primarily honeybees visiting three different plant species during two months of the summer. The dataset consists of 107,387 annotated time-lapse images from multiple cameras, including 9423 annotated insects. We present a method for detecting insects in time-lapse RGB images, which 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 on 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
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