Collision hazard detection for construction worker safety using audio surveillance

The ability to hear auditory safety cues of mobile equipment while wearing hearing protection equipment (HPE) is critical to preventing injuries and deaths in construction. Existing collision hazard detection models using proximity technologies have limited applicability due to the need for an expensive and complex deployment of sensing devices on every piece of construction equipment. This study proposes a more affordable collision prevention technology that uses audio signals to detect the presence of mobile equipment. The study addresses the problem by improving the auditory situational awareness for construction workers exposed to loud noises with a novel sound detection model that uses artificial intelligence (AI) to detect the sound of collision hazards buried in a great deal of ambient noises. This study included three phases: (1) collecting audio data of construction equipment, (2) developing a novel audio-based machine learning model for automated detection of collision hazards, and (3) conducting field experiments to investigate the system's efficiency and latency. The outcomes showed that the proposed model detects equipment correctly and can timely notify the workers of hazardous situations.