ArticleAnimals : an open access journal from MDPI2022
Study on Poultry Pose Estimation Based on Multi-Parts Detection.
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 9 papers.
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
9 citing papers in PubMed, 27 citations in OpenAlex.
- Head-Body-Tail Segmentation of Poultry 3D Point Clouds Under Different Annotation Budgets: A Comparison of PCA-Based Rules and PointNet++-Based Methods.Animals : an open access journal from MDPI · 2026Article
- EDC-YOLO-World-DB: A Model for Dairy Cow ROI Detection and Temperature Extraction Under Complex Conditions.Animals : an open access journal from MDPI · 2025Article
- Structure-Aware Multi-Animal Pose Estimation for Space Model Organism Behavior Analysis.Animals : an open access journal from MDPI · 2025Article
- Determining the scanning range of coronary computed tomography angiography based on deep learning.World journal of radiology · 2025Article
- Comprehensive analysis of the effects of high stocking density stress on broiler health.Frontiers in veterinary science · 2025Review
- MCP: Multi-Chicken Pose Estimation Based on Transfer Learning.Animals : an open access journal from MDPI · 2024Article
- Individual Pig Identification Using Back Surface Point Clouds in 3D Vision.Sensors (Basel, Switzerland) · 2023Article
- Super-resolution fusion optimization for poultry detection: a multi-object chicken detection method.Journal of animal science · 2023Article
- A High-Performance Day-Age Classification and Detection Model for Chick Based on Attention Encoder and Convolutional Neural Network.Animals : an open access journal from MDPI · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Poultry pose estimation is a prerequisite for evaluating abnormal behavior and disease prediction in poultry. Accurate pose-estimation enables poultry producers to better manage their poultry. Because chickens are group-fed, how to achieve automatic poultry pose recognition has become a problematic point for accurate monitoring in large-scale farms. To this end, based on computer vision technology, this paper uses a deep neural network (DNN) technique to estimate the posture of a single broiler chicken. This method compared the pose detection results with the Single Shot MultiBox Detector (SSD) algorithm, You Only Look Once (YOLOV3) algorithm, RetinaNet algorithm, and Faster_R-CNN algorithm. Preliminary tests show that the method proposed in this paper achieves a 0.0128 standard deviation of precision and 0.9218 ± 0.0048 of confidence (95%) and a 0.0266 standard deviation of recall and 0.8996 ± 0.0099 of confidence (95%). By successfully estimating the pose of broiler chickens, it is possible to facilitate the detection of abnormal behavior of poultry. Furthermore, the method can be further improved to increase the overall success rate of verification.
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.