Biology subjects

Tabak, M. A.

Publications and source records attributed to Tabak, M. A..

5 recordsLinked to original sources

bioRxiv2022

CameraTrapDetectoR: Automatically detect, classify, and count animals in camera trap images using artificial intelligence

Motion-activated wildlife cameras, or camera traps, are widely used in biological monitoring of wildlife. Studies using camera traps amass large numbers of images and analyzing these images can be a large burden that inhibits research progress. We trained deep learning computer vision models using data for 168 species that automatically detect, count, and classify common North American domestic and wild species in camera trap images. We provide our trained models in an R package, CameraTrapDetectoR. Three types of models are available: a taxonomic class model classifies objects as mammal (human and non-human) or avian; a taxonomic family model that recognizes 31 mammal, avian, and reptile families; a species model that recognizes 75 domestic and wild species including all North American wild cat species, bear species, and Canid species. Each model also includes a category for vehicles and empty images. The models performed well on both validation datasets and out-of-distribution testing datasets as mean average precision values ranged from 0.80 to 0.96. CameraTrapDetectoR provides predictions as an R object (a data frame) and flat file and provides the option to create plots of the original camera trap image with the predicted bounding box and label. There is also the option to apply models using a Shiny Application, with a point-and-click graphical user interface. This R package has the potential to facilitate application of deep learning models by biologists using camera traps.

ecology↗

bioRxiv2021

Automated classification of bat echolocation call recordings with artificial intelligence

Acoustic recorders are commonly used to remotely monitor and collect data on bats (Order Chiroptera). These efforts result in many acoustic recordings that must be classified by a bat biologist with expertise in call classification in order to obtain useful information. The rarity of this expertise and time constraints have prompted efforts to automatically classify bat species in acoustic recordings using a variety of learning methods. There are several software programs available for this purpose, but they are imperfect and the United States Fish and Wildlife Service often recommends that a qualified acoustic analyst review bat call identifications even if using these software programs. We sought to build a model to classify bat species using modern computer vision techniques. We used images of bat echolocation calls (i.e., plots of the pulses) to train deep learning computer vision models that automatically classify bat calls to species. Our model classifies 10 species, five of which are protected under the Endangered Species Act. We evaluated our models using standard model validation procedures, but we also performed two out-of-distribution tests. For the out-of-distribution tests, an entire dataset was withheld from the procedure before splitting the data into training and validation sets. We found that our validation accuracy (93%) and out-of-distribution accuracy (90%) were higher than when we used Kaleidoscope Pro and BCID software (65% and 61% accuracy, respectively) to evaluate the same calls. Our results suggest that our approach is effective at classifying bat species from acoustic recordings, and our trained model will be incorporated into new bat call identification software: WEST-EchoVision.

ecology↗

bioRxiv2020

rapidPop: Rapid population assessments of wildlife using camera trap data in R Shiny Applications

O_SCPLOWRAPIDC_SCPLOWPO_SCPLOWOPC_SCPLOW is a new R package for implementing occupancy models for Rapid Population Assessments (RPAs) with data from camera traps. RPAs are designed to provide quick assessments of a density index so that users can identify relative changes in density associated with changes in conditions (Schlichting et al., 2020). For example, users may want to assess if there was a change in density after an effort to cull an invasive species. O_SCPLOWRAPIDC_SCPLOWPO_SCPLOWOPC_SCPLOW provides a Shiny Application for running occupancy models with the option to include the effect of parameters on occupancy (e.g., before or after a culling operation).

ecology↗

bioRxiv2020

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

O_LIMotion-activated wildlife cameras (or "camera traps") are frequently used to remotely and non-invasively observe animals. The vast number of images collected from camera trap projects have prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. C_LIO_LIWe used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks, one that recognizes 58 species, the "species model," and one that determines if an image is empty or if it contains an animal, the "empty-animal model." C_LIO_LIOur species model and empty-animal model had accuracies of 96.8% and 97.3%, respectively. Furthermore, the models performed well on some out-of-sample datasets, as the species model had 91% accuracy on species from Canada (accuracy range 36-91% across all out-of-sample datasets) and the empty-animal model achieved an accuracy of 91-94% on out-of-sample datasets from different continents. C_LIO_LIOur software addresses some of the limitations of using machine learning to classify images from camera traps. By including many species from several locations, our species model is potentially applicable to many camera trap studies in North America. We also found that our empty-animal model can facilitate removal of images without animals globally. We provide the trained models in an R package (MLWIC2: Machine Learning for Wildlife Image Classification in R), which contains Shiny Applications that allow scientists with minimal programming experience to use trained models and train new models in six neural network architectures with varying depths. C_LI

ecology↗

bioRxiv2018

Machine learning to classify animal species in camera trap images: applications in ecology

1. Motion-activated cameras (\"camera traps\") are increasingly used in ecological and management studies for remotely observing wildlife and have been regarded as among the most powerful tools for wildlife research. However, studies involving camera traps result in millions of images that need to be analyzed, typically by visually observing each image, in order to extract data that can be used in ecological analyses.\n\n2. We trained machine learning models using convolutional neural networks with the ResNet-18 architecture and 3,367,383 images to automatically classify wildlife species from camera trap images obtained from five states across the United States. We tested our model on an independent subset of images not seen during training from the United States and on an out-of-sample (or \"out-of-distribution\" in the machine learning literature) dataset of ungulate images from Canada. We also tested the ability of our model to distinguish empty images from those with animals in another out-of-sample dataset from Tanzania, containing a faunal community that was novel to the model.\n\n3. The trained model classified approximately 2,000 images per minute on a laptop computer with 16 gigabytes of RAM. The trained model achieved 98% accuracy at identifying species in the United States, the highest accuracy of such a model to date. Out-of-sample validation from Canada achieved 82% accuracy, and correctly identified 94% of images containing an animal in the dataset from Tanzania. We provide an R package (Machine Learning for Wildlife Image Classification; MLWIC) that allows the users to A) implement the trained model presented here and B) train their own model using classified images of wildlife from their studies.\n\n4. The use of machine learning to rapidly and accurately classify wildlife in camera trap images can facilitate non-invasive sampling designs in ecological studies by reducing the burden of manually analyzing images. We present an R package making these methods accessible to ecologists. We discuss the implications of this technology for ecology and considerations that should be addressed in future implementations of these methods.

ecology↗