An Adaptive Region Proposal Network With Progressive Attention Propagation for Tiny Person Detection From UAV Images

Two-stage detectors, which consist of the multi-scale feature representations and the prediction of region proposal boxes, have been recognized as an effective paradigm for tiny object detection in Unmanned Aerial Vehicle (UAV) images. Although most previous methods primarily concentrated on developing efficient feature fusion strategies within the feature pyramid network (FPN), few studies elaborated on improving the performance of region proposal network (RPN). Conventional RPNs exhibit two key weaknesses in the majority of existing two-stage object detection approaches. Firstly, the quality of proposal boxes generated by the RPN is heavily reliant on rich feature representations extracted from the FPN backbone. Secondly, the fixed number of generated proposal boxes limits adaptability to the distribution of tiny person objects. To mitigate the aforementioned problems, in this paper we propose a novel adaptive region proposal network (ARPN) to improve the quality of the proposal boxes and generate particularly compact yet accurate proposal boxes. On one hand, a progressive attention mechanism is devised to make the ARPN focus more on prospective object regions, where a series of multi-scale front attention modules (FAM) are applied to coarsely filter out most of irrelevant background areas and a group of top-to-bottom back attention modules (BAM) aid the ARPN to finely pinpoint tiny objects of interest in a coarse-to-fine manner. On the other hand, a mini-density map, which is inspired by the philosophy of crowd counting, is elaborately designed to adaptively determine the number of region proposal boxes. This approach significantly reduces redundancy while maintaining high-quality proposal boxes. Extensive experiments verify the superiority of proposed ARPN and show obvious improvement over other competitors in terms of two performance indicators of average precision (AP) and average recall (AR). The code will be available at
https://github.com/kbzhang0505/ARPN
.