Images Of Wildlife Research Articles

Wildlife Tracker using Deep Learning

Wildlife tracker using deep learning is a project aimed at developing a system that can automatically detect wild animals using deep learning techniques. Conservationists and researchers face challenges in accurately and efficiently detecting and monitoring wildlife populations in vast and often remote habitats. Traditional methods of wildlife monitoring, such as manual surveys or camera traps, are labour-intensive, time-consuming, and may not provide real-time insights into population dynamics or threats facing wildlife species. The proposed system aims to address these challenges by developing an automated animal detection system using the YOLOv8 object detection model. The system will be trained on a diverse dataset of wildlife images, encompassing various species and environmental conditions. Upon deployment, the system will analyse input images and accurately identify and localize animals within the scene in real-time. Through this approach, the system will provide conservationists and researchers with timely and actionable information for monitoring wildlife populations, assessing habitat health, and implementing targeted conservation interventions. Evaluation of the system's performance will involve metrics such as detection accuracy, precision, recall, and processing speed, ensuring reliable and efficient wildlife detection capabilities. Overall, the proposed system seeks to enhance wildlife conservation efforts by leveraging advanced technology to improve wildlife monitoring and management practices and provide real-time alert.

Aerial Wildlife Image Repository for animal monitoring with drones in the age of artificial intelligence.

Drones (unoccupied aircraft systems) have become effective tools for wildlife monitoring and conservation. Automated animal detection and classification using artificial intelligence (AI) can substantially reduce logistical and financial costs and improve drone surveys. However, the lack of annotated animal imagery for training AI is a critical bottleneck in achieving accurate performance of AI algorithms compared to other fields. To bridge this gap for drone imagery and help advance and standardize automated animal classification, we have created the Aerial Wildlife Image Repository (AWIR), which is a dynamic, interactive database with annotated images captured from drone platforms using visible and thermal cameras. The AWIR provides the first open-access repository for users to upload, annotate, and curate images of animals acquired from drones. The AWIR also provides annotated imagery and benchmark datasets that users can download to train AI algorithms to automatically detect and classify animals, and compare algorithm performance. The AWIR contains 6587 animal objects in 1325 visible and thermal drone images of predominantly large birds and mammals of 13 species in open areas of North America. As contributors increase the taxonomic and geographic diversity of available images, the AWIR will open future avenues for AI research to improve animal surveys using drones for conservation applications. Database URL: https://projectportal.gri.msstate.edu/awir/.

High-efficiency Distributed Image Compression Algorithm Based on Soft Threshold Iteration for Wildlife Images with Wireless Image Sensor Networks

High-efficiency Distributed Image Compression Algorithm Based on Soft Threshold Iteration for Wildlife Images with Wireless Image Sensor Networks

Intelligent identification system of wild animals image based on deep learning in biodiversity conservation law

This study aims to overcome the impact of complex environmental backgrounds on the recognition of wildlife in monitoring images, thereby exploring the role of a deep learning-based intelligent wildlife recognition system in biodiversity conservation. The automatic identification of wildlife images is conducted based on convolutional neural networks (CNNs). Target detection technology, based on regression algorithms, is initially employed to extract Regions of Interest (ROI) containing wildlife from images. The wildlife regions in monitoring images are detected, segmented, and converted into ROI images. A dual-channel network model based on Visual Geometry Group 16 (VGG16) is implemented to extract features from sample images. Finally, these features are input into a classifier to achieve wildlife recognition. The proposed optimized model demonstrates superior recognition performance for five wildlife species, caribou, lynx, mule deer, badger, and antelope, compared to the dual-channel network model based on VGG16. The optimized model achieves a Mean Average Precision (MAP) of 0.714, with a maximum difference of 0.145 compared to the other three network structures, affirming its effectiveness in enhancing the accuracy of automatic wildlife recognition. The model effectively addresses the issue of low recognition accuracy caused by the complexity of background information in monitoring images, achieving high-precision recognition and holding significant implications for the implementation of biodiversity conservation laws.

Chelonia mydas detection and image extraction from noisy field recordings

<span lang="EN-US">Wildlife videography is an essential data collection method for conducting research on animals. The video recording process of an animal like the <em>Chelonia Mydas</em> turtle in its natural habitat requires the setting up of special camera traps or by performing complex camera movement to capture the animal in frame whilst the cameraman maneuvers over uneven terrain while filming. The result is hours of footage that only have the presence of the intended subject in it for seconds whilst the rest is background footage; or noisy and blurry footage that has only several usable frames among thousands of noisy and unusable ones. This presents a problem that deep learning models can help to assist, especially in detecting a wildlife subject and extracting usable data from hours of noise and background footage. This paper proposes the use of machine learning models to detect and extract wildlife images of <em>Chelonia Mydas</em> turtles to help prune through hundreds and thousands of frames from several video footages. Our paper shows that utilizing a custom model with various confidence scores can label and crop out images in noisy field video recordings of </span><em><span lang="EN-US">Chelonia Mydas </span></em><span lang="EN-US">turtles with up to 99.89% of output images correctly cropped and labeled.</span>

Improved Re-Parameterized Convolution for Wildlife Detection in Neighboring Regions of Southwest China.

To autonomously detect wildlife images captured by camera traps on a platform with limited resources and address challenges such as filtering out photos without optimal objects, as well as classifying and localizing species in photos with objects, we introduce a specialized wildlife object detector tailored for camera traps. This detector is developed using a dataset acquired by the Saola Working Group (SWG) through camera traps deployed in Vietnam and Laos. Utilizing the YOLOv6-N object detection algorithm as its foundation, the detector is enhanced by a tailored optimizer for improved model performance. We deliberately introduce asymmetric convolutional branches to enhance the feature characterization capability of the Backbone network. Additionally, we streamline the Neck and use CIoU loss to improve detection performance. For quantitative deployment, we refine the RepOptimizer to train a pure VGG-style network. Experimental results demonstrate that our proposed method empowers the model to achieve an 88.3% detection accuracy on the wildlife dataset in this paper. This accuracy is 3.1% higher than YOLOv6-N, and surpasses YOLOv7-T and YOLOv8-N by 5.5% and 2.8%, respectively. The model consistently maintains its detection performance even after quantization to the INT8 precision, achieving an inference speed of only 6.15 ms for a single image on the NVIDIA Jetson Xavier NX device. The improvements we introduce excel in tasks related to wildlife image recognition and object localization captured by camera traps, providing practical solutions to enhance wildlife monitoring and facilitate efficient data acquisition. Our current work represents a significant stride toward a fully automated animal observation system in real-time in-field applications.

Using photo editing to understand the impact of species aesthetics on support for conservation

Abstract Many threatened species suffer from a lack of conservation attention compared to others. Prioritisation of funding, research and conservation efforts seem to be driven by reasons beyond conservation need. This could be due to a ‘beauty bias’, whereby aesthetically pleasing species receive more attention. We examined how editing an image to increase a species' aesthetic appeal may impact donation choices and public attitude towards that species. We posed two research questions; first, ‘do people make different donation choices when they see original images of a species compared to when they see images of the same species that have been edited to match aesthetic preferences?’ Using hypothetical donation experiments, we asked respondents to allocate funds to the conservation of three pictured species, one ‘aesthetically appealing’, one ‘aesthetically unappealing’, and one whose image was either edited to reflect common aesthetic preferences or left unedited. Our findings suggest that images edited to make an animal more visually appealing tend to receive higher hypothetical donation amounts than original images. We also posed a second research question; ‘How do people of varying conservation expertise respond to original versus edited images of wildlife?’ To investigate this, we ran three focus groups with individuals unfamiliar with our test species, those familiar with two or more of our test species, and with conservation professionals, which showed mixed reactions both within and between groups. Focus group participants with less conservation expertise noted that edited images often seemed ‘cuter’ than unedited images, and were more likely to compare them to cartoon characters. Participants with more conservation expertise and species familiarity reported greater empathy towards unedited images, and noted that the edited images prompted an ‘uncanny valley’ response, highlighting the need for further scrutiny in how photo editing might be used in conservation messaging. Our findings support the beauty bias hypothesis and highlight that decisions on conservation support should acknowledge that less aesthetically pleasing species are disadvantaged in public attention and funding. In addition, the findings highlight the role of conservation expertise in impacting viewer reactions, as well as the ethical implications of editing images of wildlife. Read the free Plain Language Summary for this article on the Journal blog.

Wild Animal Intrusion Detection

Animal Vehicle Collision, commonly called as roadkill, is an emerging threat to humans and wild animals with increasing fatalities every year. Amid Vehicular crashes, animal actions (i.e. deer) are unpredictable and erratic on roadways. This paper unveils a newer dimension for wild animals’ auto-detection during active nocturnal hours using thermal image processing over camera car mount in the vehicle. To implement effective hot spot and moving object detection, obtained radiometric images are transformed and processed by an intelligent system. This intelligent system extracts the features of the image and subsequently detects the existence of an object of interest. The main technique to extract the features of wild animals is the Histogram of Oriented Gradient transform. The features are detected by normalizing the radiometric image and then processed by finding the magnitude and gradient of a pixel. As human populations expand and encroach upon natural habitats, conflicts between humans and wildlife become increasingly common. To mitigate the risks associated with wild animal intrusions into human settlements, an intelligent and proactive intrusion detection system is essential. This study proposes a novel approach to wild animal intrusion detection using deep learning techniques. The proposed system leverages Convolutional Neural Networks (CNNs) to analyze images captured by surveillance cameras placed in strategic locations. The deep learning model is trained on a diverse dataset of wildlife images to enable accurate identification and classification of different species. Transfer learning is employed to fine-tune the model for specific regions and scenarios, enhancing its adaptability and performance. Furthermore, the system integrates real-time image processing and edge computing to minimize latency and enable swift response to potential intrusions. The use of edge devices ensures that the intrusion detection system operates autonomously, reducing the need for constant human intervention. The effectiveness of the proposed approach is evaluated through extensive testing in real-world environments, including wildlife reserves, suburban areas, and agricultural landscapes. Performance metrics such as accuracy, precision, recall, and F1 score are employed to assess the system's ability to reliably detect and classify wild animal intrusions. The findings of this study demonstrate the feasibility and effectiveness of employing deep learning for wild animal intrusion detection. The proposed system not only enhances the safety of human populations but also contributes to wildlife conservation efforts by minimizing negative interactions between humans and animals. The scalability and adaptability of the system make it suitable for deployment in various geographic regions, thereby addressing the global challenge of human-wildlife conflicts.

DJAN: Deep Joint Adaptation Network for Wildlife Image Recognition.

Wildlife recognition is of utmost importance for monitoring and preserving biodiversity. In recent years, deep-learning-based methods for wildlife image recognition have exhibited remarkable performance on specific datasets and are becoming a mainstream research direction. However, wildlife image recognition tasks face the challenge of weak generalization in open environments. In this paper, a Deep Joint Adaptation Network (DJAN) for wildlife image recognition is proposed to deal with the above issue by taking a transfer learning paradigm into consideration. To alleviate the distribution discrepancy between the known dataset and the target task dataset while enhancing the transferability of the model's generated features, we introduce a correlation alignment constraint and a strategy of conditional adversarial training, which enhance the capability of individual domain adaptation modules. In addition, a transformer unit is utilized to capture the long-range relationships between the local and global feature representations, which facilitates better understanding of the overall structure and relationships within the image. The proposed approach is evaluated on a wildlife dataset; a series of experimental results testify that the DJAN model yields state-of-the-art results, and, compared to the best results obtained by the baseline methods, the average accuracy of identifying the eleven wildlife species improves by 3.6 percentage points.

A Forest Wildlife Detection Algorithm Based on Improved YOLOv5s.

A forest wildlife detection algorithm based on an improved YOLOv5s network model is proposed to advance forest wildlife monitoring and improve detection accuracy in complex forest environments. This research utilizes a data set from the Hunan Hupingshan National Nature Reserve in China, to which data augmentation and expansion methods are applied to extensively train the proposed model. To enhance the feature extraction ability of the proposed model, a weighted channel stitching method based on channel attention is introduced. The Swin Transformer module is combined with a CNN network to add a Self-Attention mechanism, thus improving the perceptual field for feature extraction. Furthermore, a new loss function (DIOU_Loss) and an adaptive class suppression loss (L_BCE) are adopted to accelerate the model's convergence speed, reduce false detections in confusing categories, and increase its accuracy. When comparing our improved algorithm with the original YOLOv5s network model under the same experimental conditions and data set, significant improvements are observed, in particular, the mean average precision (mAP) is increased from 72.6% to 89.4%, comprising an accuracy improvement of 16.8%. Our improved algorithm also outperforms popular target detection algorithms, including YOLOv5s, YOLOv3, RetinaNet, and Faster-RCNN. Our proposed improvement measures can well address the challenges posed by the low contrast between background and targets, as well as occlusion and overlap, in forest wildlife images captured by trap cameras. These measures provide practical solutions for enhanced forest wildlife protection and facilitate efficient data acquisition.

Klasifikasi Citra Genus panthera Menggunakan Pendekatan Deep learning Berbasis Convolutional Neural network (CNN)

This research aims to develop an image classification method for the panthera genus using a deep learning approach based on Convolutional Neural network (CNN). The panthera genus includes large species such as tigers, lions, leopards, and jaguars, which share similarities in appearance but also differences in fur patterns, body size, and habitat. Image classification of the panthera genus is important in various applications, including wildlife conservation and biological research. In this study, image datasets of tigers, lions, and leopards were collected from various sources to a total of 6,290 images. The proposed method involves image pre-processing, such as resizing, converting and normalization, and the use of a Convolutional Neural network (CNN) model to perform classification. The CNN model is implemented and trained using training data to recognize specific visual patterns in the images of each species. The results of this study show that the CNN-based deep learning approach can achieve high accuracy in the classification of panthera genus images of 85.21%. This method can correctly distinguish between tiger, lion, and leopard images based on unique visual features. In addition, the deep learning approach also offers advantages in efficiency and scalability to cope with the large number of images in the dataset. This research makes an important contribution to the development of wildlife image classification methods using a CNN-based deep learning approach.

A method for automatic identification and separation of wildlife images using ensemble learning

Camera traps have revolutionized wildlife resource surveys by enabling the acquisition of comprehensive ecosystem information. Camera traps usually produce massive images. Snapshot datasets captured in nature reserves proximate to human habitation often comprise a significant volume of human activity images containing humans or livestock. Manually identifying and labeling wildlife images from large-scale datasets is a labor-intensive task that necessitates a substantial number of professionals and incurs expensive personnel costs. By harnessing the capabilities of deep learning technology to automatically differentiate between wildlife and human activity images, ecologists can solely focus on manually labeling wildlife images that account for a minor fraction of the dataset. This strategic shift can significantly reduce personnel expenses and enhance work efficiency. Existing research usually treats human activity images as ordinary categories and utilizes species recognition methods to automatically identify and filter them. However, when human activity images overwhelmingly predominate the dataset, established species recognition methods are susceptible to misclassifying a substantial proportion of wildlife images as human activity images. This misclassification can potentially result in ecologists missing opportunities to discover or observe wildlife. To tackle this challenge, we proposed an ensemble learning method based on a conservative strategy and current mainstream deep learning frameworks to automatically identify wildlife and human activity images. We validated our method on a camera trap dataset from Lasha Mountain (LSM) in Yunnan, China. The experimental results demonstrated that our method automatically identified wildlife images from the dataset with accuracy, recall, and precision of 95.75%, 94.07%, and 83.89%, respectively. This led to an approximately 80% reduction in personnel costs.

Automated wildlife image classification: An active learning tool for ecological applications

Wildlife camera trap images are being used extensively to investigate animal abundance, habitat associations, and behavior, which is complicated by the fact that experts must first classify the images to retrieve relevant information. Artificial intelligence systems can take over this task but usually need a large number of already-labeled training images to achieve sufficient performance. This requirement necessitates human expert labor and poses a particular challenge for projects with few cameras or short durations. We propose a label-efficient learning strategy that enables researchers with small or medium-sized image databases to leverage the potential of modern machine learning, thus freeing crucial resources for subsequent analyses.Our methodological proposal is twofold: On the one hand, we improve current strategies of combining object detection and image classification by tuning the hyperparameters of both models. On the other hand, we provide an active learning system that allows training deep learning models very efficiently in terms of required manually labeled training images. We supply a software package that enables researchers to use these methods without specific programming skills and thereby ensure the broad applicability of the proposed framework in ecological practice.We show that our tuning strategy improves predictive performance, emphasizing that tuning can and must be done separately for a new data set. We demonstrate how the active learning pipeline reduces the amount of pre-labeled data needed to achieve specific predictive performance and that it is especially valuable for improving out-of-sample predictive performance.We conclude that the combination of tuning and active learning increases the predictive performance of automated image classifiers substantially. Furthermore, we argue that our work can broadly impact the community through the ready-to-use software package provided. Finally, the publication of our models tailored to European wildlife data enriches existing model bases mostly trained on data from Africa and North America.

A few-shot rare wildlife image classification method based on style migration data augmentation

Camera traps are widely used in ecological studies to capture detailed shots because they rarely disturb wildlife. However, camera traps can obtain only a small fraction of the images of rare wildlife living in wild habitats and nature reserves, often just a flash of them. Thus, the classification of rare wildlife images faces the problems of small samples, incomplete subjects, and complex backgrounds. We propose a data augmentation method for classifying these images based on a cycle-consistent adversarial network that uses two discriminators and two generators. Images augmented by manual data are input to one generator, and stylized images are input to the other generator, constrained by cycle consistency loss. The two discriminators determine whether a generated image is true or false, resulting in stylized image data. The method can accurately distinguish between similar-appearing species. A model trained by our method was used to classify six rare wildlife species with a classification accuracy of 92.2% and an F1 score of 93.3%. The deep learning techniques widely used in wildlife image recognition require large-sample datasets for adequate accuracy. Our study better solves the problem facing small-sample datasets, when large datasets of rare wildlife are not available. The proposed method can provide a reference for promoting rare wildlife conservation through digitalization and intelligence.

Giant panda face recognition based on PandaFaceNet

Individual recognition of animals via infrared camera trapping surveys is an important method for protecting and monitoring animals in the wild. However, several factors limit current survey methods used for individual animal recognition, such as the lack of accuracy and extensive time required to process data. Recently, new technologies and methods for individual recognition of animal images have been developed for rare wildlife species (e.g., giant pandas and lemurs). These new technologies require adequate and high-quality sampled images; however, it can be challenging for researchers to obtain an adequate sample size of wildlife images from the field. To overcome this problem, we proposed and tested a new small-sample individual recognition method adapted from FaceNet called PandaFaceNet, using data from a self-built giant panda (Ailuropoda melanoleuca) facial image database. We tested the proposed giant panda individual recognition method on unknown captive and wild giant panda datasets. The results showed that this method has 95.3% recognition accuracy for distinguishing among two captive giant panda facial images and 91% recognition accuracy for distinguishing among two wild giant pandas. Notably, PandaFaceNet achieves individual recognition through comparing two images and is an open-set identification method. Therefore, PandaFaceNet provides a novel method for giant panda research by opening up opportunities for analysis of small sample sizes of panda imagery data, while also providing new directions for research on rare wildlife more broadly.

Real-Time Target Detection System for Animals Based on Self-Attention Improvement and Feature Extraction Optimization

In this paper, we propose a wildlife detection algorithm based on improved YOLOv5s by combining six real wildlife images of different sizes and forms as datasets. Firstly, we use the RepVGG model to simplify the network structure that integrates the ideas of VGG and ResNet. This RepVGG introduces a structural reparameterization approach to ensure model flexibility while reducing the computational effort. This not only enhances the ability of model feature extraction but also speeds up the model computation, further improving the model’s real-time performance. Secondly, we use the sliding window method of the Swin Transformer module to divide the feature map to speed up the convergence of the model and improve the real-time performance of the model. Then, it introduces the C3TR module to segment the feature map, expand the perceptual field of the feature map, solve the problem of backpropagation gradient disappearance and gradient explosion, and enhance the feature extraction and feature fusion ability of the model. Finally, the model is improved by using SimOTA, a positive and negative sample matching strategy, by introducing the cost matrix to obtain the highest accuracy with the minimum cost. The experimental results show that the improved YOLOv5s algorithm proposed in this paper improves mAP by 3.2% and FPS by 11.9 compared with the original YOLOv5s algorithm. In addition, the detection accuracy and detection speed of the improved YOLOv5s model in this paper have obvious advantages in terms of the detection effects of other common target detection algorithms on the animal dataset in this paper, which proves that the improved effectiveness and superiority of the improved YOLOv5s target detection algorithm in animal target detection.

Comparison Of SGD, Rmsprop, and Adam Optimation In Animal Classification Using CNNs

Many measures have been taken to protect endangered species by using "camera trap" technology which is widespread in the field of technology-based nature protection field research. In this study, a machine learning-based approach is presented to identify endangered wildlife images with a data set containing 5000 images taken from Kaggle and some other sources. The Gradient Descent optimization method is often used for Artificial Neural Network (ANN) training. This method plays a role in finding the weight values that give the best output value. Three optimization methods have been implemented, namely Stochastic Gradient Descent (SGD), ADADELTA, and Adam on the Artificial Neural Network system for animal data classification. In some of the studies reviewed there are differences in the results of SGD and ADAM, which on the one hand SGD is superior, and on the one hand ADAM is superior with the appropriate learning rate. The results of this study show that the CNN method with the Adam optimization function produces the highest accuracy compared to the SGD and RMSprop optimization methods. The model trained using Adam's optimization function achieved an accuracy of 89.81% on the test, showing the feasibility of the approach.

UAS-Based Real-Time Detection of Red-Cockaded Woodpecker Cavities in Heterogeneous Landscapes Using YOLO Object Detection Algorithms

In recent years, deep learning-based approaches have proliferated across a variety of ecological studies. Inspired by deep learning’s emerging prominence as the preferred tool for analyzing wildlife image datasets, this study employed You Only Look Once (YOLO), a single-shot, real-time object detection algorithm, to effectively detect cavity trees of Red-cockaded Woodpeckers or RCW (Dryobates borealis). In spring 2022, using an unmanned aircraft system (UAS), we conducted presence surveys for RCW cavity trees within a 1264-hectare area in the Sam Houston National Forest (SHNF). Additionally, known occurrences of RCW cavity trees outside the surveyed area were aerially photographed, manually annotated, and used as a training dataset. Both YOLOv4-tiny and YOLOv5n architectures were selected as target models for training and later used for inferencing separate aerial photos from the study area. A traditional survey using the pedestrian methods was also conducted concurrently and used as a baseline survey to compare our new methods. Our best-performing model generated an mAP (mean Average Precision) of 95% and an F1 score of 85% while maintaining an inference speed of 2.5 frames per second (fps). Additionally, five unique cavity trees were detected using our model and UAS approach, compared with one unique detection using traditional survey methods. Model development techniques, such as preprocessing images with tiling and Sliced Aided Hyper Inferencing (SAHI), proved to be critical components of improved detection performance. Our results demonstrated the two YOLO architectures with tiling and SAHI strategies were able to successfully detect RCW cavities in heavily forested, heterogenous environments using semi-automated review. Furthermore, this case study represents progress towards eventual real-time detection where wildlife managers are targeting small objects. These results have implications for more achievable conservation goals, less costly operations, a safer work environment for personnel, and potentially more accurate survey results in environments that are difficult using traditional methods.

Wildlife Object Detection Method Applying Segmentation Gradient Flow and Feature Dimensionality Reduction

This work suggests an enhanced natural environment animal detection algorithm based on YOLOv5s to address the issues of low detection accuracy and sluggish detection speed when automatically detecting and classifying large animals in natural environments. To increase the detection speed of the model, the algorithm first enhances the SPP by switching the parallel connection of the original maximum pooling layer for a series connection. It then expands the model’s receptive field using the dataset from this paper to enhance the feature fusion network by stacking the feature pyramid network structure as a whole; secondly, it introduces the GSConv module, which combines standard convolution, depth-separable convolution, and hybrid channels to reduce network parameters and computation, making the model lightweight and easier to deploy to endpoints. At the same time, GS bottleneck is used to replace the Bottleneck module in C3, which divides the input feature map into two channels and assigns different weights to them. The two channels are combined and connected in accordance with the number of channels, which enhances the model’s ability to express non-linear functions and resolves the gradient disappearance issue. Wildlife images are obtained from the OpenImages public dataset and real-life shots. The experimental results show that the improved YOLOv5s algorithm proposed in this paper reduces the computational effort of the model compared to the original algorithm, while also providing an improvement in both detection accuracy and speed, and it can be well applied to the real-time detection of animals in natural environments.

An evaluation of platforms for processing camera‐trap data using artificial intelligence

Abstract Camera traps have quickly transformed the way in which many ecologists study the distribution of wildlife species, their activity patterns and interactions among members of the same ecological community. Although they provide a cost‐effective method for monitoring multiple species over large spatial and temporal scales, the time required to process the data can limit the efficiency of camera‐trap surveys. Thus, there has been considerable attention given to the use of artificial intelligence (AI), specifically deep learning, to help process camera‐trap data. Using deep learning for these applications involves training algorithms, such as convolutional neural networks (CNNs), to use particular features in the camera‐trap images to automatically detect objects (e.g. animals, humans, vehicles) and to classify species. To help overcome the technical challenges associated with training CNNs, several research communities have recently developed platforms that incorporate deep learning in easy‐to‐use interfaces. We review key characteristics of four AI platforms—Conservation AI, MegaDetector, MLWIC2: Machine Learning for Wildlife Image Classification and Wildlife Insights—and two auxiliary platforms—Camelot and Timelapse—that incorporate AI output for processing camera‐trap data. We compare their software and programming requirements, AI features, data management tools and output format. We also provide R code and data from our own work to demonstrate how users can evaluate model performance. We found that species classifications from Conservation AI, MLWIC2 and Wildlife Insights generally had low to moderate recall. Yet, the precision for some species and higher taxonomic groups was high, and MegaDetector and MLWIC2 had high precision and recall when classifying images as either ‘blank’ or ‘animal’. These results suggest that most users will need to review AI predictions, but that AI platforms can improve efficiency of camera‐trap‐data processing by allowing users to filter their dataset into subsets (e.g. of certain taxonomic groups or blanks) that can be verified using bulk actions. By reviewing features of popular AI‐powered platforms and sharing an open‐source GitBook that illustrates how to manage AI output to evaluate model performance, we hope to facilitate ecologists' use of AI to process camera‐trap data.