# SynSpill: Improved Industrial Spill Detection With Synthetic Data Source: [SynSpill: Improved Industrial Spill Detection With Synthetic Data]() Collection index: [llms.txt]() Data updated: 2026-04-24T05:00:04.604Z Authors: Aaditya Baranwal, Abdul Mueez, Jason Voelker, Guneet Bhatia, Shruti Vyas Published: 2025-08-13T20:09:58.000Z ## Abstract Large-scale Vision-Language Models (VLMs) have transformed general-purpose visual recognition through strong zero-shot capabilities. However, their performance degrades significantly in niche, safety-critical domains such as industrial spill detection, where hazardous events are rare, sensitive, and difficult to annotate. This scarcity -- driven by privacy concerns, data sensitivity, and the infrequency of real incidents -- renders conventional fine-tuning of detectors infeasible for most industrial settings. We address this challenge by introducing a scalable framework centered on a high-quality synthetic data generation pipeline. We demonstrate that this synthetic corpus enables effective Parameter-Efficient Fine-Tuning (PEFT) of VLMs and substantially boosts the performance of state-of-the-art object detectors such as YOLO and DETR. Notably, in the absence of synthetic data (SynSpill dataset), VLMs still generalize better to unseen spill scenarios than these detectors. When SynSpill is used, both VLMs and detectors achieve marked improvements, with their performance becoming comparable. Our results underscore that high-fidelity synthetic data is a powerful means to bridge the domain gap in safety-critical applications. The combination of synthetic generation and lightweight adaptation offers a cost-effective, scalable pathway for deploying vision systems in industrial environments where real data is scarce/impractical to obtain. Project Page: https://synspill.vercel.app ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Industrial Hazard Detection on Public Dataset SynSpill](): Industrial Hazard Detection; Public Dataset SynSpill; mAP@50: 84. - [Industrial Hazard Detection on Proprietary Dataset](): Industrial Hazard Detection; Proprietary Dataset; mAP@50: 71. ## Original sources and implementation [Original paper]() [PDF]()