Incorporating Multiscale Context and Task-Consistent Focal Loss into Oriented Object Detection
Xiaoliang Qian, Qiang Jian, Wei Wang, Xiwen Yao, Gong Cheng
Abstract
Oriented object detection (OOD) in remote sensing images (RSIs) aims to precisely localize and identify objects with arbitrary orientations. Two-stage OOD methods attract lots of interest due to their superior accuracy, however, they still face two major problems. First of all, the misclassification problem frequently occurs because the majority of classification strategies solely relies on the features of proposals. Secondly, most of loss functions cannot simultaneously concentrate on hard samples and boost the consistency between identification and localization, which restricts the further improvement of OOD models. To address the first problem, the multi-scale context (MSC) is incorporated into a two-stage OOD model in this paper. Specifically, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> contextual branches are added to predict the class confidence score (CCS) of each proposal and its <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> enlarged proposals which contain the MSC, and the final CCS of each proposal is determined by the mean value of above <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> + 1 CCSs. To tackle the second problem, a task-consistent focal (TF) loss is proposed. The TF loss employs the difficulty of localization as the weight of classification loss, and the difficulty of identification is used as the weight of regression loss. Concentrating on hard samples and synchronous optimization of classification and regression can be achieved by minimizing the TF loss. The ablation studies show the validity of MSC, TF and their combination. The comparison with popular OOD models demonstrates the superior performance of our model on the DOTA and DIOR-R datasets. The source code can be obtained from https://github.com/qxlzengli/MSC-TF.