iSafetyBench: A video-language benchmark for safety in industrial environment

• URL: http://arxiv.org/abs/2508.00399v1
• Date: Fri, 01 Aug 2025 07:55:53 GMT
• Title: iSafetyBench: A video-language benchmark for safety in industrial environment
• Authors: Raiyaan Abdullah, Yogesh Singh Rawat, Shruti Vyas,
• Abstract summary: iSafetyBench is a new video-language benchmark designed to evaluate model performance in industrial environments.<n>iSafetyBench comprises 1,100 video clips sourced from real-world industrial settings.<n>We evaluate eight state-of-the-art video-language models under zero-shot conditions.
• Score: 6.697702130929693
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in vision-language models (VLMs) have enabled impressive generalization across diverse video understanding tasks under zero-shot settings. However, their capabilities in high-stakes industrial domains-where recognizing both routine operations and safety-critical anomalies is essential-remain largely underexplored. To address this gap, we introduce iSafetyBench, a new video-language benchmark specifically designed to evaluate model performance in industrial environments across both normal and hazardous scenarios. iSafetyBench comprises 1,100 video clips sourced from real-world industrial settings, annotated with open-vocabulary, multi-label action tags spanning 98 routine and 67 hazardous action categories. Each clip is paired with multiple-choice questions for both single-label and multi-label evaluation, enabling fine-grained assessment of VLMs in both standard and safety-critical contexts. We evaluate eight state-of-the-art video-language models under zero-shot conditions. Despite their strong performance on existing video benchmarks, these models struggle with iSafetyBench-particularly in recognizing hazardous activities and in multi-label scenarios. Our results reveal significant performance gaps, underscoring the need for more robust, safety-aware multimodal models for industrial applications. iSafetyBench provides a first-of-its-kind testbed to drive progress in this direction. The dataset is available at: https://github.com/raiyaan-abdullah/iSafety-Bench.

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