Adaptive Augmentation of Imbalanced Class Distribution in Road Segmentation
Bibliographic record
Abstract
Deep learning has achieved significant improvements in various tasks in Computer Vision. However, acquiring a large number of the dataset is a challenge in real-world applications, especially if they are new class objects for Deep Learning. Furthermore, the distribution of classes in the dataset is often imbalanced - a bottleneck of the neural network’s performance in classification. One possible real-world application is road segmentation, which is crucial for autonomous driving and sophisticated driver assistance systems to comprehend the driving environment. Recent years have seen significant advancements in road segmentation with the help of Deep Learning. Inaccurate road boundaries and lighting fluctuations such as shadows and overexposed zones are still challenging issues. Prediction performance is also impacted by an improper class distribution, which arises because most image pixels belong to the background (negative class), while the goal is to identify road pixels (positive class). In this paper, we focus on the topic of "visual road classification," where the target is to label each pixel as containing either a road or a background. We tackle this task by implementing a novel Adaptive Augmentation algorithm by integrating with some recently suggested encoder-decoder based convolutional neural network architecture and compare the qualitative and quantitative experimental results with traditional augmentation algorithm. The proposed method uses an adaptive augmentation module to improve performance under improper class distribution conditions. Experimental results show that the suggested method achieves higher segmentation accuracy than state-of-the-art methods on the KITTI road detection benchmark datasets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".