Authors:
Assia Belbachir
1
;
Antonio Ortiz
1
;
Ahmed Belbachir
1
and
Emanuele Ciccia
2
Affiliations:
1
NORCE Research AS, Grimstad, Norway
;
2
ABS - Acciaierie Bertoli Safau S.p.A., Udine, Italy
Keyword(s):
Industrial Safety, Computer Vision, Warehouse Management, Geometric Reasoning, Steel Bar Manufacturing, Segment Anything Model, UAV.
Abstract:
In industrial warehouse environments, particularly in steel bar manufacturing scenarios, ensuring the structural stability of stacked bars is essential for both worker safety and operational efficiency. This paper presents a novel vision-based framework for automatic safety validation of outdoor storage bays using a dual-resolution implementation of the Segment Anything Model (SAM). The system processes video streams coming from drone (AUV) by combining zero-shot segmentation with geometric reasoning to assess lateral and frontal support conditions in real time. At each frame, SAM is applied at two scales to extract both fine-grained support components and large bulk regions. A morphological proximity rule reclassifies unsupported regions based on contact with multiple smaller support masks. Additionally, a frontal-view analysis computes bar-end centroids and applies a triangle-based inclusion test to determine correct placement. Experimental results on real warehouse videos demonstr
ate robust safety classification under occlusion and clutter, with interactive frame rates and no need for manual annotation. The proposed framework offers a lightweight, interpretable solution for automated safety monitoring in complex industrial environments.
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