Defending Object Detection Models against Image Distortions
Bibliographic record
Abstract
Image distortions pose a significant challenge to object detection. To address this issue, our paper introduces a novel data augmentation method that generates new samples resembling the original training images. The new sample exhibits randomly altered pixels based on a pixel distribution obtained from multiple image distortions using kernel density estimation (KDE). The main steps of our method, GSES, are generating distorted versions of each pixel of an original training image, selecting a set of pixels in each version, and then, for each selected pixel, estimating its distribution using KDE and then sampling one pixel from this distribution. By employing this approach, the new samples possess distorted pixels while maintaining a certain degree of similarity to the original image. This degree of similarity is essential to balance the accuracy of object detection models under distorted and clean images. Our approach improves the accuracy of different object detection models under 15 image distortions, such as motion blur, fog, and noise. For example, the average accuracy of YOLOv4 improves by 9.19% and 9.54 % across all 15 distortions added to the COCO and PASCAL datasets, respectively. Our method surpasses other defence methods to combat image distortions. Our ablation and stability studies show why our method performs well. Moreover, we also show that our method can be well used to improve the accuracy of image classification under 15 distortions and cross-domains. Our code is available at https://github.com/moforio/GSES/.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".