ArticleSensors (Basel, Switzerland)2020
Panoptic Segmentation of Individual Pigs for Posture Recognition.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review.Sensors (Basel, Switzerland) · 2021Pooled it
- A Multimodal Dual-Stream Framework for Sheep Behavior Recognition Using Skeletal and Local Visual Fusion.Animals : an open access journal from MDPI · 2026Article
- StaticPigDetv2: Performance Improvement of Unseen Pig Monitoring Environment Using Depth-Based Background and Facility Information.Sensors (Basel, Switzerland) · 2026Article
- Design and Verification of a Non-Contact Body Dimension Measurement System for Jiangquan Black Pigs Based on Dual-View Depth Vision.Animals : an open access journal from MDPI · 2025Article
- Instance segmentation and automated pig posture recognition for smart health management.Journal of animal science and technology · 2025Article
- Attention-Guided Instance Segmentation for Group-Raised Pigs.Animals : an open access journal from MDPI · 2023Article
- StaticPigDet: Accuracy Improvement of Static Camera-Based Pig Monitoring Using Background and Facility Information.Sensors (Basel, Switzerland) · 2022Article
- Study of a QueryPNet Model for Accurate Detection and Segmentation of Goose Body Edge Contours.Animals : an open access journal from MDPI · 2022Article
- Keypoint Detection for Injury Identification during Turkey Husbandry Using Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- EmbeddedPigCount: Pig Counting with Video Object Detection and Tracking on an Embedded Board.Sensors (Basel, Switzerland) · 2022Article
- Learn to Train: Improving Training Data for a Neural Network to Detect Pecking Injuries in Turkeys.Animals : an open access journal from MDPI · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Behavioural research of pigs can be greatly simplified if automatic recognition systems are used. Systems based on computer vision in particular have the advantage that they allow an evaluation without affecting the normal behaviour of the animals. In recent years, methods based on deep learning have been introduced and have shown excellent results. Object and keypoint detector have frequently been used to detect individual animals. Despite promising results, bounding boxes and sparse keypoints do not trace the contours of the animals, resulting in a lot of information being lost. Therefore, this paper follows the relatively new approach of panoptic segmentation and aims at the pixel accurate segmentation of individual pigs. A framework consisting of a neural network for semantic segmentation as well as different network heads and postprocessing methods will be discussed. The method was tested on a data set of 1000 hand-labeled images created specifically for this experiment and achieves detection rates of around 95% (F1 score) despite disturbances such as occlusions and dirty lenses.
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.