DFFNet: Dynamic Feature Fusion Network for Weakly Supervised Object Detection in Remote Sensing Images

Abstract

In recent years, weakly supervised object detection (WSOD) methods using only image-level labels have received increasing attention, Due to the difficulty of manually labeling large-scale remote sensing images. However, existing methods cannot generate high-quality proposals when applying proposal generation methods to RSIs. Meanwhile, these methods ignore the fact that there are a large number of objects of different scales in RSIs. To address these issues, we propose a unique end-to-end dynamic feature fusion network (DFFNet) for WSOD in RSIs. First, we propose an intersection-over-union selective search (IoU-SS) algorithm to generate high-quality proposals by preferentially merging regions with high IoU. Furthermore, we design a novel and flexible dynamic feature fusion (DFF) module to dynamically acquire features of objects at different scales based on the information of the input image. The performance of WSOD in RSIs is further improved by using high-quality proposals and dynamically fused features. Comprehensive experiments and comparisons with state-of-the-art methods on two datasets of RSIs, i.e., NWPU VHR-10.v2 and DIOR, demonstrate the superiority of our proposed method.