Evidence map›Paper›PMID 37508141›Full record

ArticleAnimals : an open access journal from MDPI2023

Contextualized Small Target Detection Network for Small Target Goat Face Detection.

Yaxin Wang, Ding Han, Liang Wang, Ying Guo, Hongwei Du

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Animals : an open access journal from MDPI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.1field-weighted citation impact, top 13% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Detection and Localization of Albas Velvet Goats Based on YOLOv4.Animals : an open access journal from MDPI · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Yaxin WangCollege of Electronic Information Engineering, Inner Mongolia University, Hohhot 010020, China.
Ding HanCollege of Electronic Information Engineering, Inner Mongolia University, Hohhot 010020, China.
Liang WangCollege of Electronic Information Engineering, Inner Mongolia University, Hohhot 010020, China.ORCID 0009-0004-4708-1079
Ying GuoCollege of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China.
Hongwei DuCollege of Electronic Information Engineering, Inner Mongolia University, Hohhot 010020, China.
Inner Mongolia University · CNFudan University · CNInner Mongolia University of Science and Technology · CN

Funding

the Major Science and Technology Project of Inner Mongolia Autonomous Region 2021ZD0019-4the National Key RD Program of China 2022YFF1300604-04the Scientific Research Project of Higher Education Institutions in Inner Mongolia Autonomous Region NJZY22337
6 · The paper itself

Abstract

With the advancement of deep learning technology, the importance of utilizing deep learning for livestock management is becoming increasingly evident. goat face detection provides a foundation for goat recognition and management. In this study, we proposed a novel neural network specifically designed for goat face object detection, addressing challenges such as low image resolution, small goat face targets, and indistinct features. By incorporating contextual information and feature-fusion complementation, our approach was compared with existing object detection networks using evaluation metrics such as F1-Score (F1), precision (P), recall (R), and average precision (AP). Our results show that there are 8.07%, 0.06, and 6.8% improvements in AP, P, and R, respectively. The findings confirm that the proposed object detection network effectively mitigates the impact of small targets in goat face detection, providing a solid basis for the development of intelligent management systems for modern livestock farms.

Indexed as

goat face detectionintelligent management systemssmall targets

Identifiers

PMID37508141
PMCPMC10376293
OpenAlexW4384929698

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.