Surface multi-spectral reflectivity and texture material recognition in non-constrained environments
Bibliographic record
Abstract
Abstract Material recognition is the process of distinguishing various materials based on their inherent physical properties. It plays a pivotal role in numerous applications, including manufacturing, recycling, and robotic handling. Conventional recognition methods predominantly employ sensor-and vision-based approaches. However, these methods often face challenges such as the similarity and variability in material appearance, environmental conditions, and geometric constraints. In this research, we introduce a multimodal, vision-based, attention-driven model for material recognition. Contrary to preceding texture-based and multi-spectral based methods, our approach harnesses both the texture and light reflection distribution characteristics intrinsic to material surfaces. The proposed method features a collocated system that combines depth, RGB, and near-infrared (Near-IR) cameras, along with infrared laser projector. This specific setup was selected to capture reflection distribution and texture across the visible-near-infrared spectrum. Subsequently, the data captured by this setup were processed by a recognition model within a fusion framework. Our results outperform previous methods in terms of accuracy when additional modalities (Depth, Near-IR, laser projectors) are available, while also exhibiting equivalent performance to top RGB-based models when solely reliant on RGB data. Thus, proving the complementarity of the added modalities with visible information.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".