A Comprehensive Survey on Object Detection Using Deep Learning
Bibliographic record
Abstract
One of the common and difficult issues in computer vision is to detect the object.Researchers have widely experimented and contributed to the performance improvement of object detection and associated tasks including object classification, localization, and segmentation over the way of the last decade of deep learning's rapid evolution.Object detectors can be broadly categorized into two groups: two stage and single stage object detectors.Two stage detectors primarily focus on selected region proposals via sophisticated architecture whereas single stage detectors concentrate on all feasible spatial region proposals for object detection via relatively easier architecture in one go.Any object detector's performance is assessed using inference time and detection accuracy.In regards to detection accuracy, two stage object detectors surpass single stage object detectors.In this survey, we present a deep literature survey on object detection methods.We also provide a summary of the comparison between two-stage and single-stage object detectors along with suggestions for further research in real-world.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".