Put Your PPE on: A Tool for Synthetic Data Generation and Related Benchmark in Construction Site Scenarios

Using Machine Learning algorithms to enforce safety in construction sites has attracted a lot of interest in recent years. Being able to understand if a worker is wearing personal protective equipment, if he has fallen in the ground, or if he is too close to a moving vehicles or a dangerous tool, could be useful to prevent accidents and to take immediate rescue actions. While these problems can be tackled with machine learning algorithms, a large amount of labeled data, difficult and expensive to obtain are required. Motivated by these observations, we propose a pipeline to produce synthetic data in a construction site to mitigate real data scarcity. We present a benchmark to test the usefulness of the generated data, focusing on three different tasks: safety compliance through object detection, fall detection through pose estimation and distance regression from monocular view. Experiments show that the use of synthetic data helps to reduce the amount of needed real data and allow to achieve good performances.