A Deformable Split Fusion Method for Object Detection in High-Resolution Optical Remote Sensing Image - Qinghe Guan; Ying Liu; Lei Chen; Guandian Li; Yang Li (Remote Sensing)
To better address the challenges of complex backgrounds, varying object sizes, and arbitrary orientations in remote sensing object detection tasks, this paper proposes a deformable split fusion method based on an improved RoI Transformer called RoI Transformer-DSF. Specifically, the deformable split fusion method contains a deformable split module (DSM) and a space fusion module (SFM). Firstly, the DSM aims to assign different receptive fields according to the size of the remote sensing object and focus the feature attention on the remote sensing object to capture richer semantic and contextual information. Secondly, the SFM can highlight the spatial location of the remote sensing object and fuse spatial information of different scales to improve the detection ability of the algorithm for objects of different sizes. In addition, this paper presents the ResNext_Feature Calculation_block (ResNext_FC_block) to build the backbone of the algorithm and modifies the original regression loss to the KFIoU to improve the feature extraction capability and regression accuracy of the algorithm. Experiments show that the mAP0.5 of this method on DOTAv1.0 and FAIR1M (plane) datasets is 83.53% and 44.14%, respectively, which is 3% and 1.87% higher than that of the RoI Transformer, and it can be applied to the field of remote sensing object detection.