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Record W4282051893 · doi:10.4108/eai.9-6-2022.174181

Review on One-Stage Object Detection Based on Deep Learning

2022· article· en· W4282051893 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueICST Transactions on e-Education and e-Learning · 2022
Typearticle
Languageen
FieldNeuroscience
TopicBrain Tumor Detection and Classification
Canadian institutionsCarleton University
Fundersnot available
KeywordsDeep learningArtificial intelligenceObject detectionComputer scienceObject (grammar)Field (mathematics)Internet of ThingsObject-class detectionComputer visionStage (stratigraphy)Face (sociological concept)The InternetPedestrianFace detectionPattern recognition (psychology)Facial recognition systemGeographyComputer securityWorld Wide WebArchaeologySociologyMathematicsGeology

Abstract

fetched live from OpenAlex

As a popular research direction in computer vision, deep learning technology has promoted breakthroughs in the field of object detection. In recent years, the combination of object detection and the Internet of Things (IoT) has been widely used in the fields of face recognition, pedestrian detecti

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.791
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.280
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it