Development of New Efficient Transposed Convolution Techniques for Flame Segmentation from UAV-captured Images
Bibliographic record
Abstract
Although Fully Convolutional Networks (FCNs) have been proven to be a very powerful tool in deep learning-based image segmentation, they are still too computationally expensive to be incorporated into mobile platforms such as Unmanned Aerial Vehicles (UAVs) for real-time performance. While significant efforts have been made to make the encoder side of a FCN more efficient, the decoder side, which involves upsampling the feature maps, is still overlooked in comparison. This paper proposes two new efficient upsampling techniques, “Reversed Depthwise Separable Transposed Convolution (RDSTC)” and “Compression-Expansion Transposed Convolution (CETC)”. U-Net architecture and UAV-captured forest pile fire images have been used to evaluate the performance of these new efficient upsampling techniques. RDSTC and CETC achieve Dice scores of 0.8815 and 0.8832 respectively, outperforming commonly used bilinear interpolation and original transposed convolution, while significantly reducing the number of upsampling computations. The results of this paper demonstrate that upsampling operation in a deep learning architecture can be made more efficient without degradation in performance.
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 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".