Detecting plains and Grevy's Zebras in the realworld

Parham, Jason; Stewart, Charles

doi:10.1109/wacvw.2016.7470122

Cited by 10 publications

(7 citation statements)

References 18 publications

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“…Researchers might want to additionally evaluate animal behaviour, differentiate between adults and young, male or female individuals, or obtain count estimates of animals in a particular image. Though some studies used models to localize and count different animals in images, they required datasets with bounding boxes or other localized annotations in order to train a model (Parham & Stewart, ; Zhang, He, Cao, & Cao, ). Norouzzadeh et al.…”

Section: Discussionmentioning

confidence: 99%

Identifying animal species in camera trap images using deep learning and citizen science

et al. 2018

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Ecologists often study wildlife populations by deploying camera traps. Large datasets are generated using this approach which can be difficult for research teams to manually evaluate. Researchers increasingly enlist volunteers from the general public as citizen scientists to help classify images. The growing number of camera trap studies, however, makes it ever more challenging to find enough volunteers to process all projects in a timely manner. Advances in machine learning, especially deep learning, allow for accurate automatic image classification. By training models using existing datasets of images classified by citizen scientists and subsequent application of such models on new studies, human effort may be reduced substantially. The goals of this study were to (a) assess the accuracy of deep learning in classifying camera trap data, (b) investigate how to process datasets with only a few classified images that are generally difficult to model, and (c) apply a trained model on a live online citizen science project. Convolutional neural networks (CNNs) were used to differentiate among images of different animal species, images of humans or vehicles, and empty images (no animals, vehicles, or humans). We used four different camera trap datasets featuring a wide variety of species, different habitats, and a varying number of images. All datasets were labelled by citizen scientists on Zooniverse. Accuracies for identifying empty images across projects ranged between 91.2% and 98.0%, whereas accuracies for identifying specific species were between 88.7% and 92.7%. Transferring information from CNNs trained on large datasets (“transfer‐learning”) was increasingly beneficial as the size of the training dataset decreased and raised accuracy by up to 10.3%. Removing low‐confidence predictions increased model accuracies to the level of citizen scientists. By combining a trained model with classifications from citizen scientists, human effort was reduced by 43% while maintaining overall accuracy for a live experiment running on Zooniverse. Ecology researchers can significantly reduce image classification time and manual effort by combining citizen scientists and CNNs, enabling faster processing of data from large camera trap studies.

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Section: Discussionmentioning

confidence: 99%

Identifying animal species in camera trap images using deep learning and citizen science

et al. 2018

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“…There are many attempts to identify animals by assigning a label to an image; however, there are limited works in the literature that focus on animal species detection, where the location of the animal is determined as well as its identification [28][29][30][31][32][33][34][35][36][37][38][39][40][41]. Some researchers used their own datasets which contain one or only a few animal species, and others used relatively small datasets (a few thousand images only) [28,31,32].…”

Section: Animal Species Detectionmentioning

confidence: 99%

“…Parham et al [39] used the YOLO detector to detect zebras from a dataset of 2500 images and created bounding boxes of Plains Zebras with an accuracy of 55.6%, and Grevy's Zebras with an accuracy of 56.6%. Zhang et al [40] created a dataset of 23 different species in both daytime color and nighttime grayscale formats from 800 camera-traps.…”

Section: Animal Species Detectionmentioning

confidence: 99%

A Performance Comparison and Enhancement of Animal Species Detection in Images with Various R-CNN Models

Ibraheam

Gebali

et al. 2021

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Object detection is one of the vital and challenging tasks of computer vision. It supports a wide range of applications in real life, such as surveillance, shipping, and medical diagnostics. Object detection techniques aim to detect objects of certain target classes in a given image and assign each object to a corresponding class label. These techniques proceed differently in network architecture, training strategy and optimization function. In this paper, we focus on animal species detection as an initial step to mitigate the negative impacts of wildlife–human and wildlife–vehicle encounters in remote wilderness regions and on highways. Our goal is to provide a summary of object detection techniques based on R-CNN models, and to enhance the performance of detecting animal species in accuracy and speed, by using four different R-CNN models and a deformable convolutional neural network. Each model is applied on three wildlife datasets, results are compared and analyzed by using four evaluation metrics. Based on the evaluation, an animal species detection system is proposed.

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“…More recently, there had been different versions of YOLO-model and their applications, e.g. YOLO-v1 model [12,13,14] , YOLO-v2 model [15,16,17] , and YOLO-v3 model [18,19] et al From the point of view of network architecture, the YOLO-v1 model contained 24 convolutional layers and two fully connected layers. The YOLO-v2 model removed the fully connected layers, but added a batch normalization behind each convolutional layer and performs normalization preprocessing for each batch of data.…”

Section: The Development Of the Yolo Modelmentioning

confidence: 99%

Automatic body region localization in 3D-CT images based on the improved YOLO model

Wang

Lin

et al. 2022

MATEC Web Conf.

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Automatic body region localization in medical three-dimensional (3D)-CT images is a critical step of computerized body-wide Automatic Anatomy Recognition (AAR) system, which can be applied for radiotherapy planning and interest slices retrieving. Currently, the complex internal structure of human body and time consuming computation are the main challenges for the localization. Therefore, this paper introduces and improves the YOLO-v3 model into the body region localization for these problems. First, seven categories of body regions in a CT volume image I are defined based on the modification version of our previous work. Second, an improved YOLO-v3 model is trained to classify each axial slice into one of the seven categories. Then, the effectiveness of the proposed method is evaluated on 3D-CT images that collected from 220 subjects. The experimental results demonstrate that the slice localizing error is less than 3 NoS (Number of slices), which is competitive to the state-of-the-art methods. Beyond this, our method is simple and computationally efficient owing to its less training time, and the average computational time for localizing a volume CT images is about 3 second, which shows potential for a further application.

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Detecting plains and Grevy's Zebras in the realworld

Cited by 10 publications

References 18 publications

Identifying animal species in camera trap images using deep learning and citizen science

Identifying animal species in camera trap images using deep learning and citizen science

A Performance Comparison and Enhancement of Animal Species Detection in Images with Various R-CNN Models

Automatic body region localization in 3D-CT images based on the improved YOLO model

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