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Rehearsal-Free Continual Learning for Emerging Unsafe Behavior Recognition in Construction Industry
Tao Wang1, Saisai Ye2, Zimeng Zhai2
1Department of Campus Security, Ocean University of China, Qingdao 266000, China.
Sensors (Basel, Switzerland)
|November 13, 2025
Summary
This study introduces an AI model for recognizing unsafe behaviors in construction, adapting to new risks without forgetting old ones. It enhances safety in Industry 5.0 by improving worker and site monitoring.
Area of Science:
- Artificial Intelligence
- Industrial Safety
- Machine Learning
Background:
- Industry 5.0 emphasizes human-centric, sustainable, and resilient industrial practices, yet construction remains labor-intensive with manual safety judgments.
- Current AI behavior recognition models struggle to adapt to new unsafe behaviors without losing knowledge of existing ones.
- Deep convolutional neural networks, while effective, are often limited by their predefined multi-way classification structure.
Purpose of the Study:
- To develop a versatile and efficient AI recognition model for identifying unsafe behaviors in construction environments.
- To enable the model to continuously learn new unsafe behaviors while retaining knowledge of previously learned categories.
- To address the limitations of traditional classifiers in adapting to emerging safety risks in dynamic industrial settings.
Main Methods:
- Proposed a continual learning approach integrating task-invariant and task-specific prompts into a pre-trained model.
- Injected prompts into specific layers of a frozen backbone to guide learning without a rehearsal buffer.
- Introduced the Split-UBR dataset, a benchmark for continual unsafe behavior recognition in construction.
Main Results:
- The proposed model demonstrated superior performance in accuracy and reduced forgetting on the Split-UBR dataset.
- Comparative experiments validated the model's effectiveness against state-of-the-art continual learning baselines.
- The method successfully recognized both new and previously learned unsafe behaviors in construction scenarios.
Conclusions:
- The developed AI model offers an effective solution for dynamic unsafe behavior recognition in the construction industry.
- The continual learning approach with complementary prompts enhances adaptability and knowledge retention in AI safety systems.
- This research contributes to safer and more resilient industrial environments within the Industry 5.0 framework.
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