ArticleAnimals : an open access journal from MDPI2020
Automated Bird Counting with Deep Learning for Regional Bird Distribution Mapping.
Article in Animals : an open access journal from MDPI, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed.
- An intelligent SCADA-integrated deep learning framework for bird-safe offshore wind farm operation.Scientific reports · 2026Article
- Amur Tiger Individual Identification Based on the Improved InceptionResNetV2.Animals : an open access journal from MDPI · 2024Article
- Using convolutional neural networks to count parrot nest-entrances on photographs from the largest known colony of Psittaciformes.Ecology and evolution · 2024Article
- Deconvolution Enhancement Keypoint Network for Efficient Fish Fry Counting.Animals : an open access journal from MDPI · 2024Article
- Optimized Small Waterbird Detection Method Using Surveillance Videos Based on YOLOv7.Animals : an open access journal from MDPI · 2023Article
- Individual Pig Identification Using Back Surface Point Clouds in 3D Vision.Sensors (Basel, Switzerland) · 2023Article
- PolarBearVidID: A Video-Based Re-Identification Benchmark Dataset for Polar Bears.Animals : an open access journal from MDPI · 2023Article
- An Advanced Chicken Face Detection Network Based on GAN and MAE.Animals : an open access journal from MDPI · 2022Article
- Eyebirds: Enabling the Public to Recognize Water Birds at Hand.Animals : an open access journal from MDPI · 2022Article
- An overview of remote monitoring methods in biodiversity conservation.Environmental science and pollution research international · 2022Review
- Study on Poultry Pose Estimation Based on Multi-Parts Detection.Animals : an open access journal from MDPI · 2022Article
- Elevated Gamma Connectivity in Nidopallium Caudolaterale of Pigeons during Spatial Path Adjustment.Animals : an open access journal from MDPI · 2022Article
- A Temporal Boosted YOLO-Based Model for Birds Detection around Wind Farms.Journal of imaging · 2021Article
- A System Using Artificial Intelligence to Detect and Scare Bird Flocks in the Protection of Ripening Fruit.Sensors (Basel, Switzerland) · 2021Article
- Reliability of animal counts and implications for the interpretation of trends.Ecology and evolution · 2021Article
- Action Recognition Using a Spatial-Temporal Network for Wild Felines.Animals : an open access journal from MDPI · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A challenging problem in the field of avian ecology is deriving information on bird population movement trends. This necessitates the regular counting of birds which is usually not an easily-achievable task. A promising attempt towards solving the bird counting problem in a more consistent and fast way is to predict the number of birds in different regions from their photos. For this purpose, we exploit the ability of computers to learn from past data through deep learning which has been a leading sub-field of AI for image understanding. Our data source is a collection of on-ground photos taken during our long run of birding activity. We employ several state-of-the-art generic object-detection algorithms to learn to detect birds, each being a member of one of the 38 identified species, in natural scenes. The experiments revealed that computer-aided counting outperformed the manual counting with respect to both accuracy and time. As a real-world application of image-based bird counting, we prepared the spatial bird order distribution and species diversity maps of Turkey by utilizing the geographic information system (GIS) technology. Our results suggested that deep learning can assist humans in bird monitoring activities and increase citizen scientists' participation in large-scale bird surveys.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.