To crop or not to crop: Comparing whole‐image and cropped classification on a large dataset of camera trap images DOI Creative Commons

Tomer Gadot,

Ștefan Istrate, HyungWon Kim

et al.

IET Computer Vision, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 24, 2024

Abstract Camera traps facilitate non‐invasive wildlife monitoring, but their widespread adoption has created a data processing bottleneck: camera trap survey can create millions of images, and the labour required to review those images strains resources conservation organisations. AI is promising approach for accelerating image review, tools are imperfect; in particular, classifying small animals remains difficult, accuracy falls off outside ecosystems which model was trained. It been proposed that incorporating an object detector into analysis pipeline may help address these challenges, benefit detection not systematically evaluated literature. In this work, authors assess hypothesis cropped from using species‐agnostic yields better than whole images. We find stage classification macro‐average F1 improvement around 25% on large, long‐tailed dataset; reproducible large public dataset smaller benchmark dataset. The describe architecture performs well both detector‐cropped demonstrate state‐of‐the‐art accuracy.

Language: Английский

Towards a Taxonomy Machine. A Training Set of 5.6 Million Arthropod Images DOI
Dirk Steinke, Sujeevan Ratnasingham, Jireh Agda

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: July 17, 2024

Abstract The taxonomic identification of organisms from images is an active research area within the machine learning community. Current algorithms are very effective for object recognition and discrimination, but they require extensive training datasets to generate reliable assignments. This study releases 5.6 million with representatives 10 arthropod classes 26 insect orders. All were taken using a Keyence VHX-7000 Digital Microscope system automatic stage permit high-resolution (4K) microphotography. Providing phenotypic data 324,000 species derived 48 countries, this release represents, by far, largest dataset standardized images. As such, well suited testing efficacy identifying specimens higher categories.

Language: Английский

Citations

2
Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge Graphs DOI
Vardaan Pahuja, Weidi Luo, Yu Gu

et al.

Published: Oct. 20, 2024

Camera traps are important tools in animal ecology for biodiversity monitoring and conservation. However, their practical application is limited by issues such as poor generalization to new unseen locations. Images typically associated with diverse forms of context, which may exist different modalities. In this work, we exploit the structured context linked camera trap images boost out-of-distribution species classification tasks traps. For instance, a picture wild could be details about time place it was captured, well biological knowledge species. While often overlooked existing studies, incorporating offers several potential benefits better image understanding, addressing data scarcity enhancing generalization. effectively heterogeneous into visual domain challenging problem. To address this, propose novel framework that transforms link prediction multimodal graph (KG). This enables seamless integration contexts recognition. We apply on iWildCam2020-WILDS Snapshot Mountain Zebra datasets achieve competitive performance state-of-the-art approaches. Furthermore, our enhances sample efficiency recognizing under-represented

Language: Английский

Citations

2
Evaluating a tandem human-machine approach to labelling of wildlife in remote camera monitoring DOI Creative Commons
Laurence A. Clarfeld, Alexej P. K. Sirén,

Brendan M. Mulhall

et al.

Ecological Informatics, Journal Year: 2023, Volume and Issue: 77, P. 102257 - 102257

Published: Aug. 10, 2023

Remote cameras ("trail cameras") are a popular tool for non-invasive, continuous wildlife monitoring, and as they become more prevalent in research, machine learning (ML) is increasingly used to automate or accelerate the labor-intensive process of labelling (i.e., tagging) photos. Human-machine hybrid tagging approaches have been shown greatly increase efficiency time tag single image). However, those potential increases hinge on extent which an ML model makes correct vs. incorrect predictions. We performed experiment using MegaDetector, that produces bounding boxes around animals, people, vehicles remote camera imagery, consider impact model's performance its ability human-labelling. Six participants tagged trail images collected from 12 sites Vermont Maine, USA (January–September 2022) three methods (one with MegaDetector's assistance two without assistance). generalized linear mixed examine influence method efficiency. found MegaDetector offers significant improvement when data compared unassisted tagging. Additionally, taken label was not statistically different approach. we gains contingent predictions, particularly 4.2% false positive 3.6% negative can slow non-hybrid These findings indicate although practitioners usually forgo production selecting due increased effort, MegaDetector-assisted offer efficient producing them. More broadly, ML-assisted opportunity analysis but assessment illuminate whether hybrid-tagging approach ultimately help hinderance.

Language: Английский

Citations

6
Bringing Back the Context: Camera Trap Species Identification as Link Prediction on Multimodal Knowledge Graphs DOI Creative Commons
Vardaan Pahuja, Weidi Luo, Yu Gu

et al.

arXiv (Cornell University), Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Camera traps are important tools in animal ecology for biodiversity monitoring and conservation. However, their practical application is limited by issues such as poor generalization to new unseen locations. Images typically associated with diverse forms of context, which may exist different modalities. In this work, we exploit the structured context linked camera trap images boost out-of-distribution species classification tasks traps. For instance, a picture wild could be details about time place it was captured, well biological knowledge species. While often overlooked existing studies, incorporating offers several potential benefits better image understanding, addressing data scarcity enhancing generalization. effectively heterogeneous into visual domain challenging problem. To address this, propose novel framework that transforms link prediction multimodal graph (KG). This enables seamless integration contexts recognition. We apply on iWildCam2020-WILDS Snapshot Mountain Zebra datasets achieve competitive performance state-of-the-art approaches. Furthermore, our enhances sample efficiency recognizing under-represented

Language: Английский

Citations

1
Metadata augmented deep neural networks for wild animal classification DOI Creative Commons

Aslak Tøn,

Ammar Ahmed, Ali Shariq Imran

et al.

Ecological Informatics, Journal Year: 2024, Volume and Issue: 83, P. 102805 - 102805

Published: Sept. 2, 2024

Camera trap imagery has become an invaluable asset in contemporary wildlife surveillance, enabling researchers to observe and investigate the behaviors of wild animals. While existing methods rely solely on image data for classification, this may not suffice cases suboptimal animal angles, lighting, or quality. This study introduces a novel approach that enhances classification by combining specific metadata (temperature, location, time, etc) with data. Using dataset focused Norwegian climate, our models show accuracy increase from 98.4% 98.9% compared methods. Notably, also achieves high metadata-only highlighting its potential reduce reliance work paves way integrated systems advance technology.

Language: Английский

Citations

1
Towards a Taxonomy Machine: A Training Set of 5.6 Million Arthropod Images DOI Creative Commons
Dirk Steinke, Sujeevan Ratnasingham, Jireh Agda

et al.

Data, Journal Year: 2024, Volume and Issue: 9(11), P. 122 - 122

Published: Oct. 25, 2024

The taxonomic identification of organisms from images is an active research area within the machine learning community. Current algorithms are very effective for object recognition and discrimination, but they require extensive training datasets to generate reliable assignments. This study releases 5.6 million with representatives 10 arthropod classes 26 insect orders. All were taken using a Keyence VHX-7000 Digital Microscope system automatic stage permit high-resolution (4K) microphotography. Providing phenotypic data 324,000 species derived 48 countries, this release represents, by far, largest dataset standardized images. As such, well suited testing efficacy identifying specimens into higher categories.

Language: Английский

Citations

1
Comparison of Automatic Classification Methods for Identification of Ice Surfaces from Unmanned-Aerial-Vehicle-Borne RGB Imagery DOI Creative Commons
Jakub Jech, Jitka Komárková, Devanjan Bhattacharya

et al.

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(20), P. 11400 - 11400

Published: Oct. 17, 2023

This article describes a comparison of the pixel-based classification methods used to distinguish ice from other land cover types. The focuses on processing RGB imagery, as these are very easy obtained. imagery was taken using UAVs and has high spatial resolution. Classical (ISODATA Maximum Likelihood) more modern approaches (support vector machines, random forests, deep learning) have been compared for image data classifications. Input datasets were created two distinct areas: Pond Skříň Baroch Nature Reserve. images classified into classes: all accuracy each verified Cohen’s Kappa coefficient, with reference values obtained via manual surface identification. Deep learning Likelihood best classifiers, over 92% in first area interest. On average, support machine classifier both areas A selected methods, which applied highly detailed UAVs, demonstrates potential their utilization satellites or aerial technologies remote sensing.

Language: Английский

Citations

2
LionSense: Redefining Wildlife Monitoring and AI-Powered YOLOv5 Lion Detection and Classification DOI
Amit Tandon,

A. Saranya,

R. Y. Shah

et al.

Lecture notes in electrical engineering, Journal Year: 2024, Volume and Issue: unknown, P. 55 - 67

Published: Jan. 1, 2024

Language: Английский

Citations

0
Particle Swarm Optimization based Prediction and Ensemble Machine Learning Classification of Wildlife Habitats DOI

E. G. Satish,

Srinivas Aluvala, Hassan M. Al–Jawahry

et al.

Published: Aug. 23, 2024

Language: Английский

Citations

0
To crop or not to crop: Comparing whole‐image and cropped classification on a large dataset of camera trap images DOI Creative Commons

Tomer Gadot,

Ștefan Istrate, HyungWon Kim

et al.

IET Computer Vision, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 24, 2024

Abstract Camera traps facilitate non‐invasive wildlife monitoring, but their widespread adoption has created a data processing bottleneck: camera trap survey can create millions of images, and the labour required to review those images strains resources conservation organisations. AI is promising approach for accelerating image review, tools are imperfect; in particular, classifying small animals remains difficult, accuracy falls off outside ecosystems which model was trained. It been proposed that incorporating an object detector into analysis pipeline may help address these challenges, benefit detection not systematically evaluated literature. In this work, authors assess hypothesis cropped from using species‐agnostic yields better than whole images. We find stage classification macro‐average F1 improvement around 25% on large, long‐tailed dataset; reproducible large public dataset smaller benchmark dataset. The describe architecture performs well both detector‐cropped demonstrate state‐of‐the‐art accuracy.

Language: Английский

Citations

0