InspecSafe-V1: A Multimodal Benchmark for Safety Assessment in Industrial Inspection Scenarios
Zeyi Liu, Shuang Liu, Jihai Min, Zhaoheng Zhang, Jun Cen, Pengyu Han, Songqiao Hu, Zihan Meng, Xiao He, Donghua Zhou

TL;DR
InspecSafe-V1 is a comprehensive multimodal dataset from real industrial inspections, enabling advanced safety assessment and scene understanding for AI systems in complex industrial environments.
Contribution
It introduces the first multimodal benchmark dataset for industrial safety assessment, covering diverse real-world scenarios with detailed annotations and multiple sensing modalities.
Findings
Provides pixel-level object annotations for safety-critical objects.
Includes seven synchronized sensing modalities for multimodal analysis.
Supports development of robust safety assessment models in industrial settings.
Abstract
With the rapid development of industrial intelligence and unmanned inspection, reliable perception and safety assessment for AI systems in complex and dynamic industrial sites has become a key bottleneck for deploying predictive maintenance and autonomous inspection. Most public datasets remain limited by simulated data sources, single-modality sensing, or the absence of fine-grained object-level annotations, which prevents robust scene understanding and multimodal safety reasoning for industrial foundation models. To address these limitations, InspecSafe-V1 is released as the first multimodal benchmark dataset for industrial inspection safety assessment that is collected from routine operations of real inspection robots in real-world environments. InspecSafe-V1 covers five representative industrial scenarios, including tunnels, power facilities, sintering equipment, oil and gas…
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Taxonomy
TopicsInfrastructure Maintenance and Monitoring · Advanced Neural Network Applications · Occupational Health and Safety Research
