Multiple Region Proposal Experts Network for Wide-Scale Remote Sensing Object Detection

Faced with the wide-scale characteristics of objects in optical remote sensing images, the current object detection models are always unable to provide satisfactory detection capabilities for remote sensing tasks. To achieve better wide-scale coverage for various remote sensing regions of interest, this article introduces a multiprediction mechanism to build a novel region generation model, namely, a multiple region proposal experts network (MRPENet). Meanwhile, to achieve both region proposal coverage and receptive field coverage of wide-scale objects, we constructed a prior design of an anchor (PDA) module and an adaptive features compensation (AFC) module to achieve the coverage of wide-scale remote sensing objects. To better utilize the multiexpert characteristics of our model, we customized a new training sample allocation strategy, dynamic scale-assigned expert learning (DSAEL), to cultivate the ability of experts to deal with objects at various scales. To the best of our knowledge, this is the first time that a multiple region proposal network (RPN) mechanism has been used in the object detection of optical remote sensing images. Extensive experiments have shown the generality and effectiveness of our MRPENet. Without bells and whistles, MRPENet achieves a new state-of-the-art (SOTA) on standard benchmarks, i.e., DOTA-v1.0 [82.02% mean average precision (mAP)], HRSC2016 (98.16% mAP), and FAIR1M-v1.0 (48.80% mAP).