OBJECT DETECTION IN OPTICAL REMOTE SENSING IMAGES BASED ON POSITIVE SAMPLE REWEIGHTING AND FEATURE DECOUPLING

The object detection head of Faster R-CNN shared by classification and localization tasks can cause greater harm to training process due to the different features required by the tasks. Besides, Faster R-CNN assigns the same weight to positive proposals, which is unfair to the positive proposal with better regression performance. Aiming at these issues, this paper proposes a novel detection framework, which consists of two important parts, i.e., a Positive Sample Reweighting (PSR) module and a Feature Decoupling (FD) module. Specifically, PSR re-weights each positive proposal according to the positional relationship between each positive proposal and its ground truth. FD employs two parallel branches to extract the weights to decouple features for classification and localization tasks. Experimental results on the DIOR and DOTA datasets show the effectiveness of our proposed method.