Enhanced Identification of Internal Casting Defects in Vehicle Wheels Using YOLO Object Detection and X-Ray Inspection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the rapidly evolving automobile industry, the safety and quality of individual vehicle components have gained paramount importance.Among these, aluminum wheels are particularly critical, given their susceptibility to internal casting defects.This study presents a novel approach to identify these defects non-destructively, employing X-ray inspection and harnessing the power of YOLO (You Only Look Once) object detection.Images of vehicle aluminum wheels were obtained via X-ray inspection, revealing the presence of internal defects.Subsequently, a periodic noise e.limination algorithm, underpinned by morphological filtering and adaptive image processing weights, was utilized to enhance the image clarity.The application of a composite cascade filter further improved the image resolution.The enhanced images were then processed using YOLO object detection, a cutting-edge technology renowned for its precision in object detection tasks.This study explores the efficacy of different YOLO model architectures in detecting and identifying internal casting defects in aluminum wheels.Our research contributes to the development of a highly accurate system for the detection of internal casting defects in vehicle wheels, offering potential improvements in vehicle safety.This methodology, pairing X-ray inspection with advanced object detection algorithms, provides a robust approach for defect identification in the production process, laying the groundwork for future advancements in vehicle component quality control.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 it