Real-Time Object Detection for the Running Train Based on the Improved YOLO V4 Neural Network
Why this work is in the frame
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Bibliographic record
Abstract
Matching the detection speed and accuracy in practical applications is considered to improve the speed of video object detection in front of trains. Lightweight convolutional neural network MobileNet and clustering ideas are combined to improve the object detection algorithm, and the MYOLO-lite model object detection algorithm is designed. The self-made object dataset of a forward-moving train is combined with the K-means clustering idea. The transcendental frame is redesigned to enhance scale adaptability. The nonmaximum suppression and loss function improvement methods of the MYOLO-lite network model are proposed for occluded tracks in front of the train, obstruction of trains, low detection accuracy of large object coincidence, and uneven distribution of positive and negative samples. The mean average precision value of the experimentally designed MYOLO-lite model object detection algorithm reaches 95.74%, with 42.04 frames per second. Detecting a picture only takes 0.024 s.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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