Feature representation improved Faster R-CNN model for high-efficiency pavement crack detection
Bibliographic record
Abstract
Two optimization methods are proposed to improve faster region-based convolutional neural network (Faster R-CNN), which are (1) restructuring Faster R-CNN's backbone network and the classification and regression (C&R) network using residual networks and (2) designing the feature ensemble structure for Faster R-CNN to combine the shallow with deep feature maps of the backbone. In addition, this paper proposed a method to evaluate the model's performance, which is pixel mean value ( P mean ) distribution of different channel feature maps, and quantitatively evaluate the feature representation capability of the model. Experimental results show that mean average precision (mAP) of the model optimized by the first method can reach 86.5%, which is 1.9% higher than that of baseline. However, mAP of the model optimized by the second method reaches 87.5%, which is 2.9% higher than the baseline model. The P mean statistics of each channel feature map extracted by different backbones show that the model accuracy is higher when the P mean of its channel feature maps is bigger, which can effectively improve the interpretability of the model accuracy.
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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".