ArticleAnimals : an open access journal from MDPI2022
Animal Detection and Classification from Camera Trap Images Using Different Mainstream Object Detection Architectures.
Article in Animals : an open access journal from MDPI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Analyzing the enhancement of CNN-YOLO and transformer based architectures for real-time animal detection in complex ecological environments.Scientific reports · 2025Pooled it
- Deep Learning-Based Monitoring of European Hamster Activity.Ecology and evolution · 2026Article
- Reannotation-Free Automatic Recognition Methods for Empty Camera Trap Images Based on MegaDetector Optimization.Animals : an open access journal from MDPI · 2026Article
- DSCF-DET: An RT-DETR-Based Framework for Fine-Grained Detection of Musk Deer and Visually Similar Artiodactyls.Biology · 2026Article
- Global Camera Trapping Inventory (GCTI) v.1.0: A worldwide inventory of camera-trapping studies for spatial biodiversity analysis.Zoological research · 2026Article
- Toward Precision Biodiversity Detection: An Edge-Deployable Framework for Mitigating Data Redundancy.Ecology and evolution · 2026Article
- RCAF-Net: Wildlife Target Detection in Complex Forest Scenarios.Animals : an open access journal from MDPI · 2026Article
- Ecological Factors at a Fine Spatial Scale Influencing Leopard (Ecology and evolution · 2026Article
- An Improved Lightweight Model for Protected Wildlife Detection in Camera Trap Images.Sensors (Basel, Switzerland) · 2025Article
- Fully automated detection and identification of CSF shunt valves using YOLOv8 and a class-based reference image assignment as a safety mechanism.Scientific reports · 2025Article
- AnimalAI: An Open-Source Web Platform for Automated Animal Activity Index Calculation Using Interactive Deep Learning Segmentation.Animals : an open access journal from MDPI · 2025Article
- AI-Powered Plant Science: Transforming Forestry Monitoring, Disease Prediction, and Climate Adaptation.Plants (Basel, Switzerland) · 2025Review
- Research trends in livestock facial identification: a review.Journal of animal science and technology · 2025Review
- An Ecologist-Friendly R Workflow for Expediting Species-Level Classification of Camera Trap Images.Ecology and evolution · 2024Article
- A Survey on Sensor Failures in Autonomous Vehicles: Challenges and Solutions.Sensors (Basel, Switzerland) · 2024Review
- Herbivorous fish feeding dynamics and energy expenditure on a coral reef: Insights from stereo-video and AI-driven 3D tracking.Ecology and evolution · 2024Article
- Monitoring Endangered and Rare Wildlife in the Field: A Foundation Deep Learning Model Integrating Human Knowledge for Incremental Recognition with Few Data and Low Cost.Animals : an open access journal from MDPI · 2023Article
- A Lightweight Automatic Wildlife Recognition Model Design Method Mitigating Shortcut Learning.Animals : an open access journal from MDPI · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Camera traps are widely used in wildlife surveys and biodiversity monitoring. Depending on its triggering mechanism, a large number of images or videos are sometimes accumulated. Some literature has proposed the application of deep learning techniques to automatically identify wildlife in camera trap imagery, which can significantly reduce manual work and speed up analysis processes. However, there are few studies validating and comparing the applicability of different models for object detection in real field monitoring scenarios. In this study, we firstly constructed a wildlife image dataset of the Northeast Tiger and Leopard National Park (NTLNP dataset). Furthermore, we evaluated the recognition performance of three currently mainstream object detection architectures and compared the performance of training models on day and night data separately versus together. In this experiment, we selected YOLOv5 series models (anchor-based one-stage), Cascade R-CNN under feature extractor HRNet32 (anchor-based two-stage), and FCOS under feature extractors ResNet50 and ResNet101 (anchor-free one-stage). The experimental results showed that performance of the object detection models of the day-night joint training is satisfying. Specifically, the average result of our models was 0.98
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.