DENSE CONTRASTIVE LEARNING BASED OBJECT DETECTION FOR REMOTE SENSING IMAGES

Supervised learning based object detectors suffer from the high cost and difficulty of labeling datasets. Self- supervised learning methods require no manual annotations. However, the misalignment between the pretext task designed for image classification and the downstream task affects the detection performance. Therefore, this paper proposes a self-supervised dense contrastive learning method to improve performance of object detection in remote sensing images. Specifically, first, Swin Transformer substitutes popular CNN to extract features of augmented multiple views. Second, global and local features are extracted using parallel global and dense projector heads, respectively. Third, a predictor head is added to increase the nonlinear transformations in the network. Extensive experiments on the NWPU VHR-10 dataset show that the proposed method outperforms two representative strong baseline methods, including MoCoV2 and DenseCL.