ArticleScientific reports2023
Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Estimating the density of raccoon dogs (Nyctereutes procyonoides) and water deer (Hydropotes inermis) using thermal real-time drone surveys and distance sampling.Scientific reports · 2026Article
- Closing the air gap: the use of drones for studying wildlife ecophysiology.Biological reviews of the Cambridge Philosophical Society · 2025Review
- Hierarchical image classification using transfer learning to improve deep learning model performance for amazon parrots.Scientific reports · 2025Article
- Article
- On the move: Influence of animal movements on count error during drone surveys.Ecology and evolution · 2024Article
- Evaluating Thermal Infrared Drone Flight Parameters on Spider Monkey Detection in Tropical Forests.Sensors (Basel, Switzerland) · 2024Article
- Aerial Wildlife Image Repository for animal monitoring with drones in the age of artificial intelligence.Database : the journal of biological databases and curation · 2024Article
- Using Sparse Parts in Fused Information to Enhance Performance in Latent Low-Rank Representation-Based Fusion of Visible and Infrared Images.Sensors (Basel, Switzerland) · 2024Article
- Expert teacher based on foundation image segmentation model for object detection in aerial images.Scientific reports · 2023Article
- Intelligent Detection Method for Wildlife Based on Deep Learning.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
8 authors.
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Abstract
Visible and thermal images acquired from drones (unoccupied aircraft systems) have substantially improved animal monitoring. Combining complementary information from both image types provides a powerful approach for automating detection and classification of multiple animal species to augment drone surveys. We compared eight image fusion methods using thermal and visible drone images combined with two supervised deep learning models, to evaluate the detection and classification of white-tailed deer (Odocoileus virginianus), domestic cow (Bos taurus), and domestic horse (Equus caballus). We classified visible and thermal images separately and compared them with the results of image fusion. Fused images provided minimal improvement for cows and horses compared to visible images alone, likely because the size, shape, and color of these species made them conspicuous against the background. For white-tailed deer, which were typically cryptic against their backgrounds and often in shadows in visible images, the added information from thermal images improved detection and classification in fusion methods from 15 to 85%. Our results suggest that image fusion is ideal for surveying animals inconspicuous from their backgrounds, and our approach uses few image pairs to train compared to typical machine-learning methods. We discuss computational and field considerations to improve drone surveys using our fusion approach.
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