Training Viola–Jones detectors for 3D objects based on fully synthetic data for use in rescue missions with UAV

In this paper, the problem of training the Viola–Jones detector for 3D objects is considered on the example of an inflatable life raft PSN-10. The detector is trained on a fully synthetic training dataset. The paper discusses in detail the methods of modelling an inflatable life raft, water surface, various weather conditions. As a feature space, we use edge Haar-like features, which allow training the detector that is resistant to various lighting conditions. To increase the computational efficiency, the L1 norm is used to calculate the magnitude of the image gradient. The performance of the trained detector is estimated on real data obtained during the rescue operation of the trawler “Dalniy Vostok”. The proposed method for training the Viola–Jones detectors can be successfully used as a component of hardware and software “assistants” of the UAV.