Using computer vision, image analysis and UAVs for the automatic recognition and counting of common cranes (Grus grus)

Chen, Assaf; Jacob, Moran; Shoshani, Gil; Charter, Motti

doi:10.1016/j.jenvman.2022.116948

Cited by 24 publications

(19 citation statements)

References 30 publications

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“…However, activity for endothermic animals is often greater in crepuscular periods, which could improve detection, but might also cause errors in double-counting animals 14 , 45 . For thermal surveys, early mornings provide the coolest temperatures compared to other times of day, even in warm environments where the image background approaches or exceeds the surface temperatures of endothermic animals 6 , 7 , 15 , 17 . Thus, conducting surveys in the morning in warm environments will likely maximize the benefit of heat contrast with target endothermic animals for thermal images and detection of these animals in visible images.…”

Section: Discussionmentioning

confidence: 99%

Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys

Krishnan

Jones

Elmore

et al. 2023

Sci Rep

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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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Section: Discussionmentioning

confidence: 99%

Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys

Krishnan

Jones

Elmore

et al. 2023

Sci Rep

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“…In addition, the classification performance is improved by using the Fruit Fly Optimization (FFO) technique to perfect the OKELM model's weight parameter. The experimental findings showed that the RDL-RTOD strategy is superior to the newer methods in many respects [ 24 ].…”

Section: Existing Surveymentioning

confidence: 99%

Visual image design of the internet of things based on AI intelligence

Tian

2023

Heliyon

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“…Guidelines for using drones to study animals usually suggest that small drone sizes (<2 kg), implementing further take‐off distances from the subject/s, higher flight heights, slow speeds and horizontal rather than vertical approaches can reduce the disturbance imposed on birds, but these responses can vary between species (Barr et al., 2020 ; Duporge et al., 2021 ; Lyons et al., 2018 ; Marchowski, 2021 ; Sorrell et al., 2023 ; Vas et al., 2015 ; Weimerskirch et al., 2018 ; Wilson et al., 2021 ). Drone use in crane research and monitoring is in its infancy, with studies generally using drones to estimate population densities (e.g., Sandhill Cranes Grus canadensis (Stark et al., 2017 ), Siberian Cranes Leucogeranus leucogeranus (Wen et al., 2021 ) and Common Cranes ( Grus grus ) (Chen et al., 2023 )). Exploratory work investigating 33 bird species has shown that drones generally impart minimal disturbance when counting populations; however, the responses of Common Cranes towards drones in this study were inconclusive (Marchowski, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Drones as a tool to study and monitor endangered Grey Crowned Cranes (Balearica regulorum): Behavioural responses and recommended guidelines

Demmer,

McIntyre

2024

Ecology and Evolution

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Crane populations are declining worldwide, with anthropogenically exacerbated habitat loss emerging as the primary causal threat. The endangered Grey Crowned Crane (Balearica regulorum) is the least studied of the three crane species that reside in southern Africa. This data paucity hinders essential conservation planning and is primarily due to ineffective monitoring methods and this species' use of inaccessible habitats. In this study, we compared the behavioural responses of different Grey Crowned Crane social groupings to traditional on‐foot monitoring methods and the pioneering use of drones. Grey Crowned Cranes demonstrated a lower tolerance for on‐foot monitoring approaches, allowing closer monitoring proximity with drones (22.72 (95% confidence intervals ‐ 13.75, 37.52) m) than on‐foot methods (97.59 (86.13, 110.59) m) before displaying evasive behaviours. The behavioural response of flocks was minimal at flight heights above 50 m, whilst larger flocks were more likely to display evasive behaviours in response to monitoring by either method. Families displayed the least evasive behaviours to lower flights, whereas nesting birds were sensitive to the angles of drone approaches. Altogether, our findings confirm the usefulness of drones for monitoring wetland‐nesting species and provide valuable species‐specific guidelines for monitoring Grey Crowned Cranes. However, we caution future studies on wetland breeding birds to develop species‐specific protocols before implementing drone methodologies.

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Using computer vision, image analysis and UAVs for the automatic recognition and counting of common cranes (Grus grus)

Cited by 24 publications

References 30 publications

Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys

Fusion of visible and thermal images improves automated detection and classification of animals for drone surveys

Visual image design of the internet of things based on AI intelligence

Drones as a tool to study and monitor endangered Grey Crowned Cranes (Balearica regulorum): Behavioural responses and recommended guidelines

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