Deep Object Ranking for Template Matching
Bibliographic record
Abstract
Pick-and-place is an important task in robotic manipulation. In industry, template-matching approaches are often used to provide the level of precision required to locate an object to be picked. However, if a robotic workstation is to handle numerous objects, brute-force template-matching becomes expensive, and is subject to notoriously hard-to-tune thresholds. In this paper, we explore the use of Deep Learning methods to speed up traditional methods such as template matching. In particular, we employed a Single Shot Detection (SSD) and a Residual Network (ResNet) for object detection and classification. Classification scores allows the re-ranking of objects so that template matching is performed in order of likelihood. Tests on a dataset containing 10 industrial objects demonstrated the validity of our approach, by getting an average ranking of 1.37 for the object of interest. Moreover, we tested our approach on the standard Pose dataset which contains 15 objects and got an average ranking of 1.99. Because SSD and ResNet operates essentially in constant time in a Graphics Processor Unit, our approach is able to reach near-constant time execution. We also compared the F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> scores of LINE-2D, a state-of-the-art template matching method, using different strategies (including our own) and the results show that our method is competitive to a brute-force template matching approach. Coupled with near-constant time execution, it therefore opens up the possibility for performing template matching for databases containing hundreds of objects.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".