Implementation and adoption of AI-based camera systems for pedestrian detection on construction sites: field insights on barriers and enablers

Abstract

Collisions between mobile industrial equipment and pedestrian workers remain a major source of serious injuries and fatalities on construction sites, accounting for 8% of reported fatal accidents in Quebec, Canada. In response, proximity detection systems, including artificial intelligence (AI)-based camera systems providing automated pedestrian detection, have been introduced to support collision avoidance. Adoption of AI-based camera systems is increasing due to their logistical simplicity compared with RFID-based solutions, but their use on construction sites remains limited and exploratory. Consequently, field-based evidence on how these systems interact with real construction activities and provide added value remains limited. This study examines barriers and enablers to the implementation and adoption of AI-based camera systems for pedestrian detection on construction sites.The study is based on a multi-case qualitative field investigation conducted across six construction sites involving four companies and twelve types of mobile equipment. Data collection included on-site field observations supported by video recordings and semi-structured interviews with mobile equipment operators, pedestrian workers, managers, and technology vendors. An inductive thematic analysis identified interrelated barriers and enablers shaping implementation and adoption, including detection reliability, installation and configuration practices, interface design, worksite organization, and worker involvement. These findings provide field-based insights into real-world use and show that adoption depends on alignment across technical, operational, organizational, and user-related factors.

Keywords: Human Factors, Construction Safety, AI-enabled Safety Technologies, Field-based Studies

DOI: 10.54941/ahfe1007925