Shark Detection from Aerial Imagery Using Region-Based CNN, a Study

Sharma, Nabin; Scully‐Power, Paul; Blumenstein, Michael

doi:10.1007/978-3-030-03991-2_23

Cited by 35 publications

(32 citation statements)

References 20 publications

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“…State-ofthe art computer-vision methods allow for information to be extracted from large volumes of photographs by classifying the content into predefined classes (such as landscapes), by recognizing discrete objects (such as species), or by grouping together similar images for human analysts. These approaches have recently been used to monitor species (Sharma et al 2018) and to examine aesthetic preferences (Seresinhe et al 2017(Seresinhe et al , 2018 and human activities and preferences (Richards & Tunçer 2018;Gosal et al 2019;Koylu et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Exploring human–nature interactions in national parks with social media photographs and computer vision

Väisänen

Hiippala

Toivonen

2021

Conservation Biology

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Understanding the activities and preferences of visitors is crucial for managing protected areas and planning conservation strategies. Conservation culturomics promotes the use of user-generated online content in conservation science. Geotagged social media content is a unique source of in situ information on human presence and activities in nature. Photographs posted on social media platforms are a promising source of information, but analyzing large volumes of photographs manually remains laborious. We examined the application of state-of-the-art computer-vision methods to studying human-nature interactions. We used semantic clustering, scene classification, and object detection to automatically analyze photographs taken in Finnish national parks by domestic and international visitors. Our results showed that human-nature interactions can be extracted from user-generated photographs with computer vision. The different methods complemented each other by revealing broad visual themes related to level of the data set, landscape photogeneity, and human activities. Geotagged photographs revealed distinct regional profiles for national parks (e.g., preferences in landscapes and activities), which are potentially useful in park management. Photographic content differed between domestic and international visitors, which indicates differences in activities and preferences. Information extracted automatically from photographs can help identify preferences among diverse visitor groups, which can be used to create profiles of national parks for conservation marketing and to support conservation strategies that rely on public acceptance. The application of computer-vision methods to automatic content analysis of photographs should be explored further in conservation culturomics, particularly in combination with rich metadata available on social media platforms.

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Section: Introductionmentioning

confidence: 99%

Exploring human–nature interactions in national parks with social media photographs and computer vision

Väisänen

Hiippala

Toivonen

2021

Conservation Biology

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“…By testing this pipeline with the Tiny-YOLOv3 algorithm we found a speed improvement of about 44% (7.22 FPS with the Python algorithm). It is true that similar performance was achieved in [7], but in this case the number of classes is more than doubled, Faster-R-CNN detector, that is the CNN architecture implemented in [7], was 5 times slower than Tiny-YOLOv3. Moreover, as shown before, the detector only reaches the top speed of 30 FPS in its native C implementation.…”

Section: Resultsmentioning

confidence: 66%

“…This paper took inspiration from [7], moving forward by exploring the possibility to perform the classification task onboard a UAV. This step allows to optimise the patrolling activity, by letting a fleet of UAV to move autonomously, to get closer to targets not clearly identified or to monitor areas in which the population density is higher.…”

Section: Methodsmentioning

confidence: 99%

“…The same research led to a second publication, aimed at applying the same architecture to estimate mammals population in images acquired from higher altitudes [9]. Despite the configuration was exactly the same of [7], the achieved results were far below, with an average precision of 0.28. The authors claimed that the application of a non-maxima suppression algorithm might have improved the whole pipeline performance by neglecting the objects overlapping for more than 75%.…”

Section: Literature Reviewmentioning

confidence: 98%

“…However, the results of this study still depend on the capacity to identify objects and extract their relevant features. The introduction of the CNN allowed to by-pass the initial anomalies detection algorithms, as shown in [7]. These networks were developed to analyse data that were positionally related (e.g.…”

Section: Literature Reviewmentioning

confidence: 99%

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Adaptive Detection Tracking System for Autonomous UAV Maritime Patrolling

Panico

Fragonara

Al‐Rubaye

2020

2020 IEEE 7th International Workshop on Metrology for AeroSpace (MetroAeroSpace)

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Nowadays, Unmanned Aerial Vehicles (UAVs) are considered reliable autonomous systems suitable for several autonomous applications, especially for target detection and tracking. Although significant developments were achieved in object detection systems over the last decades using the deep learning technique known as Convolutional Neural Networks (CNN), there are still research gaps in this area. In this paper, we present a new object detection tracking algorithm that can apply on low power consuming processing boards. In particular, we analysed a specific application scenario in which a UAV patrols coastlines and autonomously classifies different kind of marine objects. Current state of the art solutions propose centralised architectures or flying systems with human in the loop, making the whole system poorly efficient and not scalable. On the contrary, applying Artificial Intelligence (AI) detection system that runs on commercial Graphics Processing Units (GPUs) makes UAVs potentially more efficient than humans (especially for dull tasks like coastline patrolling) and the whole system becomes easily scalable because each UAV can fly independently and the Ground Control Station does not represent a bottleneck. To deal with this task, a database consisting of more than 115.000 images was created to train and test several CNN architectures. Furthermore, an adaptive detection tracking system was introduced to make the whole system faster by optimizing the balance between new detections and tracking existing targets. The proposed solution is based on the measure of the tracking confidence and the frame similarity, by means of the Structural SIMilarity (SSIM) index, computed both globally and locally. Finally, the developed algorithms were tested on a realistic scenario by means of a UAV test-bed.

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Using very‐high‐resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes

Duporge

Isupova

Reece

et al. 2020

Remote Sens Ecol Conserv

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Satellites allow large‐scale surveys to be conducted in short time periods with repeat surveys possible at intervals of <24 h. Very‐high‐resolution satellite imagery has been successfully used to detect and count a number of wildlife species in open, homogeneous landscapes and seascapes where target animals have a strong contrast with their environment. However, no research to date has detected animals in complex heterogeneous environments or detected elephants from space using very‐high‐resolution satellite imagery and deep learning. In this study, we apply a Convolution Neural Network (CNN) model to automatically detect and count African elephants in a woodland savanna ecosystem in South Africa. We use WorldView‐3 and 4 satellite data –the highest resolution satellite imagery commercially available. We train and test the model on 11 images from 2014 to 2019. We compare the performance accuracy of the CNN against human accuracy. Additionally, we apply the model on a coarser resolution satellite image (GeoEye‐1) captured in Kenya, without any additional training data, to test if the algorithm can generalize to an elephant population outside of the training area. Our results show that the CNN performs with high accuracy, comparable to human detection capabilities. The detection accuracy (i.e., F2 score) of the CNN models was 0.78 in heterogeneous areas and 0.73 in homogenous areas. This compares with the detection accuracy of the human labels with an averaged F2 score 0.77 in heterogeneous areas and 0.80 in homogenous areas. The CNN model can generalize to detect elephants in a different geographical location and from a lower resolution satellite. Our study demonstrates the feasibility of applying state‐of‐the‐art satellite remote sensing and deep learning technologies for detecting and counting African elephants in heterogeneous landscapes. The study showcases the feasibility of using high resolution satellite imagery as a promising new wildlife surveying technique. Through creation of a customized training dataset and application of a Convolutional Neural Network, we have automated the detection of elephants in satellite imagery with accuracy as high as human detection capabilities. The success of the model to detect elephants outside of the training data site demonstrates the generalizability of the technique.

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Shark Detection from Aerial Imagery Using Region-Based CNN, a Study

Cited by 35 publications

References 20 publications

Exploring human–nature interactions in national parks with social media photographs and computer vision

Exploring human–nature interactions in national parks with social media photographs and computer vision

Adaptive Detection Tracking System for Autonomous UAV Maritime Patrolling

Using very‐high‐resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes

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