Foreground Fusion-Based Liquefied Natural Gas Leak Detection Framework From Surveillance Thermal Imaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A leak detection and repair survey (LDAR) is essential to ensure a reliable and safe liquefied natural gas (LNG) supply. Modern LDAR systems deploy numerous fixed thermal imaging devices to automatically monitor the risk of potential leaks empowered by computational intelligence frameworks. Existing frameworks employ either background subtraction-based (BGS-based) or deep neural network-based (DNN-based) frameworks for LNG leak detection from thermal images. However, the BGS-based frameworks feature high sensitivity to perceive LNG emissions with low precision. On the contrary, the DNN-based frameworks can precisely classify the LNG leak after training while the sensitivity is low. Additionally, conventional DNN-based frameworks are difficult in modeling non-rigid objects such as LNG gas due to limited perceptive fields. Therefore, this study proposes a hybrid framework, namely foreground fusion-based gas detection (FFBGD), combining the advantages of BGS-based and DNN-based detectors for improved detection robustness through newly introduced concept of information fusion to LNG industries. Specifically, a foreground fusion network (FFN) is designed to fuse information of original thermal and foreground images after BGS based on the visual attention mechanism. Meanwhile, several advanced modules, i.e. deformable convolution, feature pyramid network, and cascade region-of-interest (ROI) head are adopted to enhance leak detection by offering better perceptive fields. Extensive experiments are carried out in this study to demonstrate the significant improvement brought by the proposed FFBGD over leak detection accuracy and robustness. Hence, the proposed solution can be deployed in energy facilities and enable reliable visual surveillance of LNG leaks.
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it