A Multiple Prediction Mechanisms Ensemble for Complex Remote Sensing Scenes

摘要

Facing complex remote sensing scenes, detection models with single detection mechanisms cannot always provide satisfactory detection capabilities. In order to obtain better detection performance in various remote sensing scenes, this paper constructs a novel ensemble model, namely: the multiple prediction mechanisms ensemble (MPME). In order to improve the feature representation ability and region recognition ability of the ensemble model, we build the ensemble of feature pyramids (EFP) and the ensemble of detection heads (EDH) respectively. In order to further improve the detection accuracy of the ensemble model, we propose a training strategy (k-Nearest Loss Learning), so that each sub-detector does not need to learn a trade-off among all training samples, and also reduces the possibility of model over-fitting. The experimental results show that our MPME is a more efficient and effective ensemble model. Compared with other ensemble models, our MPME has a faster detection speed and better detection accuracy. Compared with other state-of-the-art detectors, our detector also achieves superior detection performance.