Collision Hazard Detection for Construction Worker Safety Using Audio Surveillance

Augmented Hearing of Auditory Safety Cues for Construction Workers: A Systematic Literature Review

“…Existing frameworks for detecting safety cues solely rely upon a single type of information, for example, high-frequency filtering [ 84 ], sound identify classification [ 85 , 86 ], or direction of arrival of sound [ 87 ]. Using the information individually is insufficient for evaluating hazardousness in construction that requires a simultaneous consideration of many factors, including the size of the equipment in contact with, the breaking speed of a machine, the average reaction time of a worker, and the speed of the worker.…”

“…Recent studies by Refs. [ 85 , 86 ] developed sound classification models that can distinguish between mobile equipment and stationary equipment to support collision hazard detection. These studies collected and synthesized the sound of construction equipment at different signal-to-noise ratios and used the dataset to develop a machine learning model using a CNN for automated detection of mobile equipment occurrence.…”

“…Compared with earlier efforts, Refs. [ 85 , 86 ] offer superior advancement, as their models are able to deal with complex soundscapes with overlapping sound sources, including mobile and stationary equipment and background noise (e.g., workers communicating, materials’ movement, and street noises). Another study that utilized equipment sounds for collision hazard assessment was performed by Ref.…”

“…The determination of sound location can supplement the sound classifiers developed by Refs. [ 85 , 86 ] to enable a more comprehensive assessment of the hazardousness-based situations. This information is vital for construction workers and safety engineers to precisely reduce false alarms by only notifying workers if they are in a danger zone based on the distance calculated using the direction of the hazard.…”

“…Therefore, some performance metrics, such as recall, cost, computational power, and data usage, can still be used for a fair comparison. In terms of performance comparison, they all yielded a competitive recall of 99% [ 85 ], 98% [ 96 ], 99% [ 85 , 86 ], and 100% [ 97 ]. Cost comparison was another metric to measure past success in preventing struck-by hazards with mobile equipment.…”