Methodology on robot-based complex surface processing using 2D and 3D visual combination
Bibliographic record
Abstract
Despite the significant development of automated equipment in the seafood industry, including the automated processing equipment for fish and crab, tremendous challenges accompany. Low processing capability for complex seafood surfaces is a paradigm, leading to heavy reliance on manual labor. The research is driven by the urgent need for robotic processing of the complex shape of seafood, which has a promising potential in the processing of objects with random, flexible, and complex shapes. In this paper, we are proposing a novel approach to processing complex surfaces designed for the seafood industry by combining 2D and 3D visual information based on point cloud segmentation. Porcupine crabs—a species of king crab in the family Lithodidae living in the Canadian Atlantic Ocean—have complex surfaces with long, sharp spines and are chosen as the case study. The unique feature of the Porcupine crabs poses substantial challenges to the conventional visual processing method in terms of low accuracy and efficiency. On the other hand, using our method, the crab feature has been successfully recognized and processed by cutting the spines. The robot spine removal tool path is generated based on the extracted spine features. Simulation results using Robot Operating System (ROS) and experimental tests have validated the robustness of the proposed method.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".