A Task-Balanced Multiscale Adaptive Fusion Network for Object Detection in Remote Sensing Images

Object detection is essential in the interpretation of remote sensing images. However, the blurred background and objects with vast variances are identified as the two main challenges of the task. We propose a novel detector adapted to complicated background and multi-scale objects, namely, task-balanced multi-scale adaptive fusion network (TMAFNet), targeting directly on the above two challenges. Firstly, a depth separable global context module (DSGC) is constructed to understand contextual relations among pixels from a global perspective, which is extraordinarily necessary to distinguish objects from the environment. Most importantly, DSGC reduces the computational cost by decoupling the acquisition of global information into single-channel global interaction and multi-channel single-point interaction. Secondly, in order to eliminate disturbance and enhance representation ability of objects, hidden recursive feature pyramid network (HRFPN) is explored, which encodes the information of difference before and after using the multi-scale fusion. HRFPN is proven to enhance the target features by reducing the background noise. Thirdly, a semi-coupling task-balanced head (SCTB) is presented to guarantee the consistency of detection. We have conducted comprehensive experiments on several publicly available datasets, and the results illustrate that our modules improve adaptability and robustness of the network, leading to a new state-of-the-art.