Assistive computer vision technologies have the potential to significantly enhance workplace safety by enabling early detection of hazards and supporting proactive risk management. However, the development of such systems is constrained by the absence of comprehensive video datasets and clearly defined tasks that capture real-world hazard conditions. This study formulates pre-incident hazard recognition as a distinct assistive-vision problem, focusing on identifying unsafe states that precede incidents rather than the incidents themselves. To address this problem, we propose the Workplace Hazards Dataset (WHD), a balanced and diverse set of real-world videos representing five universal hazard categories in varied workplace settings. Furthermore, we establish a standardized benchmarking framework that evaluates state-of-the-art convolutional and transformer-based video models on both performance and inference-latency metrics to assess real-time feasibility. Experimental results show that the Multiscale Vision Transformer (MViT 16 × 4) achieves the highest accuracy (74.1%) while maintaining efficient inference speed, highlighting the importance of balancing recognition accuracy with processing time. Overall, this work defines a new benchmark task for assistive computer vision and provides the foundation for developing real-time hazard recognition systems that enhance safety and efficiency in high-risk environments. • Introducing Workplace Hazards Dataset (WHD), the first real-world hazard video dataset. • Benchmarking state-of-the-art CNN and transformer-based models for hazard recognition. • Evaluating dataset and model effectiveness in identifying real-world workplace hazards.
Ayoubi et al. (2026) studied this question.