  The majority of fatalities and traumatic injuries in heavy industries involve
mobile plant and vehicles, often resulting from a lapse of attention or
communication. Existing approaches to hazard identification include the use of
human spotters, passive reversing cameras, non-differentiating proximity
sensors and tag based systems. These approaches either suffer from problems of
worker attention or require the use of additional devices on all workers and
obstacles. Whilst computer vision detection systems have previously been
deployed in structured applications such as manufacturing and on-road vehicles,
there does not yet exist a robust and portable solution for use in unstructured
environments like construction that effectively communicates risks to relevant
workers. To address these limitations, our solution, the Toolbox Spotter (TBS),
acts to improve worker safety and reduce preventable incidents by employing an
embedded robotic perception and distributed HMI alert system to augment both
detection and communication of hazards in safety critical environments. In this
paper we outline the TBS safety system and evaluate its performance based on
data from real world implementations, demonstrating the suitability of the
Toolbox Spotter for applications in heavy industries.
