The utility of automated Electro-Optical systems for measuring marine mammal densities

Schoonmaker, Jon; Podobna, Yuliya; Boucher, Cynthia; Sofianos, James; Oakley, Daniel; Medeiros, Dustin; Lopez, Jasmine

doi:10.1109/oceans.2010.5664431

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…Animal pixels were differentiated from non-animal pixels based on whether the spectral value of the pixel was above or a below a set threshold (Chabot & Francis, 2016). For example, light/dark spectral thresholds have been used to identify white birds on dark backgrounds in RGB (Chabot & Bird, 2012) and between hot rabbits and a cold background in infrared imagery (Christiansen et al, 2014), while blue/green thresholds have been used to distinguish between marine mammals and water (Podobna et al, 2010;Schoonmaker et al, 2010Schoonmaker et al, , 2011Selby et al, 2011).…”

Section: Automated Detection Methodsmentioning

confidence: 99%

Automated detection of wildlife using drones: Synthesis, opportunities and constraints

et al. 2021

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1. Accurate detection of individual animals is integral to the management of vulnerable wildlife species, but often difficult and costly to achieve for species that occur over wide or inaccessible areas or engage in cryptic behaviours. There is a growing acceptance of the use of drones (also known as unmanned aerial vehicles, UAVs and remotely piloted aircraft systems, RPAS) to detect wildlife, largely because of the capacity for drones to rapidly cover large areas compared to ground survey methods. While drones can aid the capture of large amounts of imagery, detection requires either manual evaluation of the imagery or automated detection using machine learning algorithms.While manual evaluation of drone-acquired imagery is possible and sometimes necessary, the powerful combination of drones with automated detection of wildlife in this imagery is much faster and, in some cases, more accurate than using human observers.Despite the great potential of this emerging approach, most attention to date has been paid to the development of algorithms, and little is known about the constraints around successful detection (P. W. J. Baxter, and G. Hamilton, 2018, Ecosphere, 9, e02194).2. We reviewed studies that were conducted over the last 5 years in which wildlife species were detected automatically in drone-acquired imagery to understand how technological constraints, environmental conditions and ecological traits of target species impact detection with automated methods.3. From this review, we found that automated detection could be achieved for a wider range of species and under a greater variety of environmental conditions than reported in previous reviews of automated and manual detection in droneacquired imagery. A high probability of automated detection could be achieved efficiently using fixed-wing platforms and RGB sensors for species that were large and occurred in open and homogeneous environments with little vegetation or variation in topography while infrared sensors and multirotor platforms were necessary to successfully detect small, elusive species in complex habitats.4. The insight gained in this review could allow conservation managers to use drones and machine learning algorithms more accurately and efficiently to conduct abundance data on vulnerable populations that is critical to their conservation.

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Section: Automated Detection Methodsmentioning

confidence: 99%

Automated detection of wildlife using drones: Synthesis, opportunities and constraints

et al. 2021

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“…(), Schoonmaker et al. (, ) and Selby et al. () developed techniques to detect whales at or near the water surface based on spectral characteristics, and Maire et al.…”

Section: Overview Of Image‐analysis Techniquesmentioning

confidence: 99%

“…Other efforts have focused on marine mammals that, like offshore birds, tend to be sparsely distributed over large expanses that render manual image review very labor-intensive. Podobna et al (2010), Schoonmaker et al (2010Schoonmaker et al ( , 2011 and Selby et al (2011) developed techniques to detect whales at or near the water surface based on spectral characteristics, and Maire et al (2013Maire et al ( , 2015 and Mejias et al (2013) developed OBIA approaches similar to those described above for birds to detect dugongs (Dugong dugon) in high-resolution UAS images. Maussang et al (2015) proposed general-purpose algorithms for detecting miscellaneous marine animals in ocean-surface images.…”

Section: Automated Bird Counts In Aerial Imagesmentioning

confidence: 99%

Computer-automated bird detection and counts in high-resolution aerial images: a review

2016

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Bird surveys conducted using aerial images can be more accurate than those using airborne observers, but can also be more time‐consuming if images must be analyzed manually. Recent advances in digital cameras and image‐analysis software offer unprecedented potential for computer‐automated bird detection and counts in high‐resolution aerial images. We review the literature on this subject and provide an overview of the main image‐analysis techniques. Birds that contrast sharply with image backgrounds (e.g., bright birds on dark ground) are generally the most amenable to automated detection, in some cases requiring only basic image‐analysis software. However, the sophisticated analysis capabilities of modern object‐based image analysis software provide ways to detect birds in more challenging situations based on a variety of attributes including color, size, shape, texture, and spatial context. Some techniques developed to detect mammals may also be applicable to birds, although the prevalent use of aerial thermal‐infrared images for detecting large mammals is of limited applicability to birds because of the low pixel resolution of thermal cameras and the smaller size of birds. However, the increasingly high resolution of true‐color cameras and availability of small unmanned aircraft systems (drones) that can fly at very low altitude now make it feasible to detect even small shorebirds in aerial images. Continued advances in camera and drone technology, in combination with increasingly sophisticated image analysis software, now make it possible for investigators involved in monitoring bird populations to save time and resources by increasing their use of automated bird detection and counts in aerial images. We recommend close collaboration between wildlife‐monitoring practitioners and experts in the fields of remote sensing and computer science to help generate relevant, accessible, and readily applicable computer‐automated aerial photographic census techniques.

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“…Imagery or visual observation approaches are limited by atmospheric conditions and greatly affected by illumination conditions [19], [20], [21]. Infrared (IR) imagery has the potential to improve upon visual observations by enabling nighttime detections [22], [23]. Despite its limitations, visual imagery or vision is an attractive solution given it offers a rich source of information.…”

Section: Introductionmentioning

confidence: 99%

Detection of dugongs from unmanned aerial vehicles

Maire

Mejias²,

Hodgson

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Monitoring and estimation of marine populations is of paramount importance for the conservation and management of sea species. Regular surveys are used to this purpose followed often by a manual counting process. This paper proposes an algorithm for automatic detection of dugongs from imagery taken in aerial surveys. Our algorithm exploits the fact that dugongs are rare in most images, therefore we determine regions of interest partially based on color rarity. This simple observation makes the system robust to changes in illumination. We also show that by applying the extended-maxima transform on red-ratio images, submerged dugongs with very fuzzy edges can be detected. Performance figures obtained here are promising in terms of degree of confidence in the detection of marine species, but more importantly our approach represents a significant step in automating this type of surveys.

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The utility of automated Electro-Optical systems for measuring marine mammal densities

Cited by 5 publications

References 4 publications

Automated detection of wildlife using drones: Synthesis, opportunities and constraints

Automated detection of wildlife using drones: Synthesis, opportunities and constraints

Computer-automated bird detection and counts in high-resolution aerial images: a review

Detection of dugongs from unmanned aerial vehicles

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