Lobster detection using an Embedded 2D Vision System with a FANUC industrual robot
Bibliographic record
Abstract
In this paper, two vision-based systems approaches are studied in order to guide the FANUC robot arm ‘FANUC LR Mate 200iD/7L’ to locate and manipulate lobsters. Different experiments are carried out using this robot in Robotics, Electronics and Industry 4.0 Laboratory of the Université de Moncton. The first approach aims to test the ability of the integrated vision system iRvision of the FANUC robot in detecting lobster body. The Geometric Pattern Matching (GPM) Locator and the Curved Surface Matching (CSM) Locator are studied and tested for this purpose. The second approach consists of a computer vision solution based on the YOLOv4 object detection algorithm, which was implemented and tested on the Nvidia Jetson NX embedded platform. Experimental results showed that, on the one hand, the iRVision system using GPM Locator has failed to detect lobster body parts. On the other hand, the CSM Locator has detected lobster body parts with a lower score detection and has exceeded 1 second for time detection. However, the embedded vision system based on the YOLOv4 model detected the main lobster body parts and achieved 99.29% for the main average precision (mAP) and 0.1806 second for detection time on the Nvidia jetson Xavier NX embedded platform.
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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 | 0.000 | 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 | 0.000 |
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".