  Human safety has always been the main priority when working near an
industrial robot. With the rise of Human-Robot Collaborative environments,
physical barriers to avoiding collisions have been disappearing, increasing the
risk of accidents and the need for solutions that ensure a safe Human-Robot
Collaboration. This paper proposes a safety system that implements Speed and
Separation Monitoring (SSM) type of operation. For this, safety zones are
defined in the robot's workspace following current standards for industrial
collaborative robots. A deep learning-based computer vision system detects,
tracks, and estimates the 3D position of operators close to the robot. The
robot control system receives the operator's 3D position and generates 3D
representations of them in a simulation environment. Depending on the zone
where the closest operator was detected, the robot stops or changes its
operating speed. Three different operation modes in which the human and robot
interact are presented. Results show that the vision-based system can correctly
detect and classify in which safety zone an operator is located and that the
different proposed operation modes ensure that the robot's reaction and stop
time are within the required time limits to guarantee safety.
