Yolov4 High-Speed Train Wheelset Tread Defect Detection System Based on Multiscale Feature Fusion
Bibliographic record
Abstract
The Yolov4 detection algorithm does not sufficiently extract local semantic and location information. This study aims to solve this problem by proposing a Yolov4-based multiscale feature fusion detection system for high-speed train wheel tread defects. First, multiscale feature maps are obtained from a feature extraction backbone network. The proposed multiscale feature fusion network then fuses the underlying features of the original three scales. These fused features contain more defect semantic information and location details. Based on the fused features, a path aggregation network is used to fuse feature maps at different resolutions, with an improved loss function that speeds up the convergence of the network. Experimental results show that the proposed method is effective at detecting defects in the wheel treads of high-speed trains.
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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".