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The majority of fatalities and traumatic injuries in heavy industries involvemobile plant and vehicles, often resulting from a lapse of attention orcommunication. Existing approaches to hazard identification include the use ofhuman spotters, passive reversing cameras, non-differentiating proximitysensors and tag based systems. These approaches either suffer from problems ofworker attention or require the use of additional devices on all workers andobstacles. Whilst computer vision detection systems have previously beendeployed in structured applications such as manufacturing and on-road vehicles,there does not yet exist a robust and portable solution for use in unstructuredenvironments like construction that effectively communicates risks to relevantworkers. To address these limitations, our solution, the Toolbox Spotter (TBS),acts to improve worker safety and reduce preventable incidents by employing anembedded robotic perception and distributed HMI alert system to augment bothdetection and communication of hazards in safety critical environments. In thispaper we outline the TBS safety system and evaluate its performance based ondata from real world implementations, demonstrating the suitability of theToolbox Spotter for applications in heavy industries.

Toolbox Spotter: A Computer Vision System for Real World Situational Awareness in Heavy Industries