LeakGPT: detecting water pipe leaks using vision language models
Bibliographic record
Abstract
Aging water infrastructures pose significant risks to water systems, and detecting water pipe leaks is essential because leaks can lead to service shutdowns and disrupt daily life and industrial operations. Among various inspection techniques, acoustic methods have gained attention due to their ability to detect small leaks that other approaches might miss. Typically, audio signals are converted into spectrograms to enhance the leak-related features. Traditionally, human experts manually analyze these spectrograms which is a both time-consuming and labor-intensive process. More recent approaches employ neural networks to analyze spectrograms automatically. However, these methods often produce results that are hard to interpret, making it challenging for human experts to trust their conclusions. In contrast, vision language models (VLMs) have shown strong capabilities in interpreting images, while their potential for analyzing spectrograms remains unexplored. Herein, we fine-tuned a VLM, which we refer to as LeakGPT, using a customized dataset collected from field water pipeline inspection. Since current VLMs lack specialized knowledge in this domain, we address this gap by leveraging a large language model (LLM) to generate question-answer pairs from manually labeled spectrograms, forming a domain-specific dataset. This dataset is then used to fine-tune three foundational VLMs (Llama3.2, Qwen2, and Pixtral) to establish benchmark performance. Experimental results indicate that LeakGPT, fine-tuned on the Pixtral foundation model, can effectively interpret spectrograms and detect water pipe leaks, indicating its potential as a useful tool in water pipe leak inspection.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".