Construction site safety remains a critical challenge, with thousands of accidents annually resulting in severe injuries, financial losses, and project delays. Recent advancements in artificial intelligence and blockchain technology offer innovative approaches to safety management. The integration of these technologies presents a meaningful potential to address persistent safety challenges in the construction industry. This study proposes a solution that combines computer vision and blockchain to enhance accident prevention, risk mitigation, and regulatory compliance by tracking and extracting critical safety-related information. In addition to Artificial Intelligence-driven techniques, a mathematical model based on partial differential equations is introduced to interpret information in blurred images within relevant contexts. Preliminary numerical tests demonstrate promising results for detection under optimal conditions. However, performances in degraded scenarios, such as those affected by visual distortions, require further investigations to ensure robust and reliable application in real-world construction environments
A possible approach to integrate Computer Vision and Blockchain for Construction Safety Management
Marciano C.;
2025-01-01
Abstract
Construction site safety remains a critical challenge, with thousands of accidents annually resulting in severe injuries, financial losses, and project delays. Recent advancements in artificial intelligence and blockchain technology offer innovative approaches to safety management. The integration of these technologies presents a meaningful potential to address persistent safety challenges in the construction industry. This study proposes a solution that combines computer vision and blockchain to enhance accident prevention, risk mitigation, and regulatory compliance by tracking and extracting critical safety-related information. In addition to Artificial Intelligence-driven techniques, a mathematical model based on partial differential equations is introduced to interpret information in blurred images within relevant contexts. Preliminary numerical tests demonstrate promising results for detection under optimal conditions. However, performances in degraded scenarios, such as those affected by visual distortions, require further investigations to ensure robust and reliable application in real-world construction environmentsI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
