Accurate foreign object detection for the coal preparation industry based on computer vision and deep learning algorithms
Bibliographic record
Abstract
Accurate detection of foreign objects during industrial coal preparation is crucial to ensuring the safety of equipment and personnel, as well as maintaining the green utilization of the coal product. The complexity of industrial coal preparation environments presents challenges for vision-based foreign object detection. This work introduces the SHA-DH-YOLOX algorithm, designed to enhance detection accuracy. The proposed algorithm boosts three notable improvements. First, the Shuffle Attention mechanism (SHA) is integrated into the YOLOX backbone to strengthen the feature extraction of essential information from input images. Second, the Dynamic head (Dyhead) is introduced into the feature fusion to enhance the detector’s representation capability, improving scale-, spatial-, and task-awareness. Third, the original Intersection over Union (IoU) loss function is replaced with SCYLLA-IoU (SIoU) to achieve more accurate bounding boxes and enhanced training stability. These improvements work collaboratively with YOLOX-M, resulting in it outperforming state-of-the-art baseline models, including YOLOv5, YOLOv6, YOLOv7, YOLOv8, and original YOLOX. The developed SHA-DH-YOLOX algorithm improves AP50 by 1.87 to 7.97% compared to baseline models of equivalent size. Robustness tests further affirm the stability of the SHA-DH-YOLOX model when facing diverse and challenging scenarios. This pioneering work provides valuable tools for achieving safe and reliable coal preparation practices.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".