TL;DR
Suggestions for ensuring safe person detection using AI in industrial environments are offered, including suggestions for ensuring safe person detection using AI in industrial environments.
Artificial intelligence, when responsibly implemented, represents a transformative adjunct to traditional safety practices – capable of significantly improving construction site safety performance globally – but it must be deployed in tandem with organizational commitment, worker training, and robust safety cultures.
Auto repair workshops are high-risk environments where workers face frequent accidents caused by slips, trips, heavy lifting, exposure to hazardous substances, and malfunctioning equipment. Despite advances in occupational health and safety (OHS) regulations, accident rates in Romania and across the European Union remain above average, highlighting the need for new preventive strategies. This paper explores how artificial intelligence (AI) can be systematically applied to reduce operational and safety risks in auto repair shops. Accident statistics and a detailed risk assessment of slips, trips, and falls provide the foundation for analyzing AI-based solutions, including computer vision, augmented reality, smart floors, voice assistance, and predictive analytics. A six-month pilot project conducted in a Romanian auto repair shop demonstrated a 28% reduction in minor accidents, faster hazard detection, and measurable economic benefits, while also revealing challenges such as false positives, resistance to change, and maintenance requirements. The findings confirm that AI can move workplace safety from reactive to preventive management by enabling real-time monitoring, proactive alerts, and predictive forecasting. The study concludes that AI, when combined with training and organizational adaptation, can significantly enhance worker protection, operational efficiency, and sustainability in the automotive repair sector.
Industrial environments are characterized by complex interactions among human operators, machinery, materials, and information systems, creating diverse safety challenges. Conventional safety management approaches predominantly rely on periodic inspections, manual monitoring, and reactive intervention strategies, which often fail to provide timely hazard detection and rapid emergency response. The emergence of Industry 4.0 has fundamentally transformed industrial safety management through the integration of cyber-physical systems (CPS), the Industrial Internet of Things (IIoT), artificial intelligence (AI), machine learning (ML), digital twins, and automated control technologies. This review presents a comprehensive analysis of industrial hazard identification and automated safety control systems by synthesizing research published between 2013 and 2026. The study examines industrial hazards, enabling technologies, communication architectures, intelligent analytics methods, and automated response mechanisms. Furthermore, it explores the role of Safety 4.0 and Industry 5.0 paradigms in promoting human-centric, sustainable, and resilient industrial operations. Key challenges related to interoperability, cybersecurity, data quality, scalability, ethical concerns, and workforce adaptation are critically evaluated. Finally, future research opportunities, including explainable artificial intelligence, digital twins, immersive technologies, and autonomous safety systems, are discussed.
Modern warehouse environments demand intelligent monitoring systems that ensure both operational safety and security. However, existing surveillance solutions remain largely reactive, relying on manual observation or isolated detection mechanisms that fail to address complex real-world challenges such as occluding theft behaviors, unsafe item placement, and varying lighting conditions. This research proposes an integrated vision-based framework that unifies warehouse safety monitoring and theft detection using advanced computer vision and deep learning techniques. The system combines object detection, human pose identification, human activity recognition (HAR), and multi-camera dynamic person re-identification in occlusion scenarios for theft detection and geometry-aware risk analysis and automated shelf edge detection to detect hazardous shelf conditions in real-time. Theft-related activities such as loitering and abnormal human– item interactions are identified using activity sequences, while safety risks such as overhanging or fallen items are detected through segmentation-based object recognition and spatial boundary analysis. To enhance robustness, the framework incorporates multiple cameras for handling occluded situations and temporal stabilization techniques to reduce detection instability and false alerts. By integrating behavioral analysis with environmental risk assessment, the proposed system transforms traditional passive surveillance into a proactive monitoring solution. The framework aims to improve warehouse safety, reduce product damage, and enable early detection of theft through accurate, real-time alerts. This research contributes a scalable, multi-modal approach that addresses key limitations in existing systems, including lack of context awareness, poor occlusion handling, and absence of unified safety-security monitoring.
The problems addressed in this paper are platform architecture, multimodal data fusion and LLM grounding mechanism in the Nigerian industrial system in addition to evaluation metrics, security control, human-in-the-loop validation of safety alerts and the limitations to real-world deployment.
Recent advances in artificial intelligence (AI) and computer vision technologies have enabled practical applications in industrial environments where safety, quality, and productivity are critical. To support both researchers and practitioners, this survey categorizes AI-based vision systems by their functional objectives rather than algorithmic classification. Specifically, representative implementations are organized across key application areas including safety monitoring, product quality inspection, assembly line support, and worker productivity enhancement. Most of the surveyed studies are in the manufacturing and construction sectors, where real-world deployments have demonstrated measurable improvements. Unlike many previous reviews, this survey focuses on image-centric applications, using visually interpretable outputs such as photographs, video frames, and real-world examples to illustrate the on-site usability of AI vision systems. It also organizes prior work by functional roles and practical deployment considerations, rather than algorithm-centric evaluations, to provide practitioners with actionable insights for industrial adoption.