High-Precision and Fast Inference for Infrared Small Target Detection through Semantic Gap Reduction
Bibliographic record
Abstract
In recent years, significant advancements have been made in the field of infrared small target detection (IRSTD), largely driven by developments in deep learning and computer vision. Deep learning-based methods have demonstrated substantial improvements in both accuracy and inference speed compared to traditional approaches, enabling their integration into real-time embedded systems. However, many data-driven techniques rely on complex network architectures to process large volumes of intricate data, resulting in additional computational overhead. To enhance the efficiency of IRSTD, we propose an improvement based on the classical segmentation framework, introducing a semantic gap elimination module (SGEM) to reduce the level-to-level semantic gap. This enhancement improves the stability and performance of IRSTD. Notably, our method does not rely on complex network architectures, allowing it to outperform other deep learning-based methods in terms of computational efficiency. It also exceeds the performance of the fastest methods, achieving more than a threefold increase in the frames per second (FPS). Furthermore, comparative experiments demonstrate the effectiveness of our approach, showing superior performance over recent methods in both segmentation and localization accuracy.
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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 | 0.002 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".