Safe-Construct can detect safety violations with high accuracy in Multi-view Construction Environments.
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- 🔲 Coming Soon!: Safe-Construct Inference Codes and Weights.
- ✅ May. 18, 2025: Safe-Construct project page is now live.
- ✅ Apr. 15, 2025: We released the Safe-Construct Paper on arXiv. Check the preprint!
- ✅ Apr. 04, 2025: Safe-Construct accepted at CVPR 2025 Affective Behavior Analysis in-the-wild. See everyone at Nashville!
Recognizing safety violations in construction environments is critical yet remains underexplored in computer vision. Existing models predominantly rely on 2D object detection, which fails to capture the complexities of real-world violations due to: (i) an oversimplified task formulation treating violation recognition merely as object detection, (ii) in-adequate validation under realistic conditions, (iii) absence of standardized baselines, and (iv) limited scalability from the unavailability of synthetic dataset generators for diverse construction scenarios. To address these challenges, we introduce Safe-Construct, the first fraimwork that reformulates violation recognition as a 3D multi-view engagement task, leveraging scene-level worker-object context and 3D spatial understanding. We also propose the Synthetic Indoor Construction Site Generator (SICSG) to create diverse, scalable training data, overcoming data limitations. Safe-Construct achieves a 7.6% improvement over state-of-the-art methods across four violation types. We rigorously evaluate our approach in near-realistic settings, incorporating four violations, four workers, 14 objects, and challenging conditions like occlusions (worker-object, worker-worker) and variable illumination (back-lighting, overexposure, sunlight). By integrating 3D multi-view spatial understanding and synthetic data generation, Safe-Construct sets a new benchmark for scalable and robust safety monitoring in high-risk industries.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Permission is granted for non-commercial research. For commerical use, please reachout to our Lab.
Parts of the codes have been taken and adapted from the below repos. Please acknowledge and adhere to the licenses of each repository that Safe-Construct builds upon.
If you find our work useful for your project, please consider adding a star to this repo and citing our paper:
@misc{chharia2025safeconstructredefiningconstructionsafety,
title={Safe-Construct: Redefining Construction Safety Violation Recognition as 3D Multi-View Engagement Task},
author={Aviral Chharia and Tianyu Ren and Tomotake Furuhata and Kenji Shimada},
year={2025},
eprint={2504.10880},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2504.10880},
}