Image dataset for foreign object detection in iron ore conveyor belt systems
Bibliographic record
Abstract
This paper presents a dataset of high-speed recordings of iron ore flowing on a laboratory-scale conveyor belt, captured with top-down videography and organized to highlight both regular operation and the presence of foreign objects. The conveyor belt measures 35 cm in width by 1.10 m in length. It operates at adjustable speeds and is powered by an electric motor to transport hematite and selected contaminants, such as wood pieces or plastic fragments. An NVIDIA Jetson TX2, equipped with its onboard OV5693 camera, recorded the footage at 120 frames per second in 1280 × 720 resolution, using a GStreamer pipeline to stream the video directly to disk. Individual frames were then extracted and sorted into subfolders, distinguishing normal operations from segments containing manually introduced anomalies. Additional subsets further categorize objects by type, enabling adaptation to various detection or classification approaches. This resource is intended to facilitate comparative evaluations of image-based detection approaches in a controlled mining context while also supporting extended uses in computer vision research related to industrial material transportation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".