Integration of Computer Vision and IOT for Real-Time Safety Monitoring in Construction Equipment: A Review

ABSTRACT Construction sites are highly hazardous due to heavy machinery, human error, and lack of real-time monitoring. The use of Artificial Intelligence (AI) offers new opportunities to improve safety through automated and proactive monitoring of equipment and workers [1]. The proposed AI-based system integrates computer vision, IoT sensors, machine learning, and predictive analytics to detect unsafe actions, equipment failures, and risky conditions. Cameras and wearable sensors track worker proximity and equipment status. AI algorithms can identify issues such as missing PPE, operator fatigue, or unauthorized entry into danger zones[6]. Machine learning analyzes parameters like vibration, temperature, and load to predict equipment malfunction. When risks are detected, the system sends alerts instantly to operators and safety managers [2]. This AI-powered monitoring reduces accidents, prevents expensive downtime, and supports real-time decision-making. Cloud data storage helps maintain long-term safety records and ensures compliance with safety standards. Although challenges include data privacy, initial cost, and the need for quality datasets, the long-term benefits—reduced accidents, improved efficiency, and enhanced worker safety—make the system highly valuable [3]. Overall, AI-based safety monitoring is a step toward achieving zero-accident construction environments. Keywords: AI-powered safety monitoring, construction equipment, computer vision, IoT sensors, machine learning, predictive analytics, worker safety