Warm Liquid Spill Detection and Tracking Using Thermal Imaging
Bibliographic record
Abstract
Detection of liquid spill is a crucial and effective task to maintain safety and protection in various environments. Thermal imaging as a passive imaging modality working in different lighting conditions and even through smoke can be advantageously used to detect liquid spill in challenging conditions. Deep learning-based object detectors are well-established techniques to detect and localize different objects or phenomena in a variety of image modalities, however they require large scale databases with bounding box annotation in order to be trained from scratch. In this work, we present, evaluate, and compare three different methods to address the unavailability of substantial datasets dedicated to liquid spill detection from thermal images in the context of health and safety prevention. A Flir A35 thermal camera is used to collect data for the experiments. The three methods are based respectively on a conventional image processing algorithm using watershed segmentation, a weakly supervised approach using Gradient Class Activation Mapping, and an unsupervised deep learning approach for salient object detection guided by motion. No pixel level annotation is required for the proposed approaches. The work demonstrates that a conventional image processing approach, achieving an average precision and an average recall as high as 0.83 and 0.72 respectively, can reliably detect and localize warm liquid spill in sequences of thermal images.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".