Assessment of conformal use of personal protective equipment by object and human pose recognition

International audienceOn construction sites, workers are exposed to a wide range of hazards. To reduce risk and prevent severe work accidents, the use of PPE (Personal Protective Equipment), such as hard hat or harness has been adopted by the industry. Monitoring the use of PPE is an inherently difficult task since its protective effectiveness depends on whether it is conformally worn. In recent years, the advent of deep learning for the tasks of object detection and human pose analysis has enabled the rise of discriminative and all-purpose PPE detectors. However, most of earlier works solely focus on detecting the equipment irrespective of their positioning. By assessing conformal PPE usage based on the combined output of object detection and articulated human pose extraction we can combine learning with domain expertise, which allows a straightforward, in-field application of the approach that attains high quantitative performance

RoboCup@Home Education 2020 Best Performance: RoboBreizh, a modular approach

Every year, the Robocup@Home competition challenges teams and robots' abilities. In 2020, the RoboCup@Home Education challenge was organized online, altering the usual competition rules. In this paper, we present the latest developments that lead the RoboBreizh team to win the contest. These developments include several modules linked to each other allowing the Pepper robot to understand, act and adapt itself to a local environment. Up-to-date available technologies have been used for navigation and dialogue. First contribution includes combining object detection and pose estimation techniques to detect user's intention. Second contribution involves using Learning by Demonstrations to easily learn new movements that improve the Pepper robot's skills. This proposal won the best performance award of the 2020 RoboCup@Home Education challenge