Context Feature Integration and Balanced Sampling Strategy for Small Weak Object Detection in Remote-Sensing Imagery

Deep learning has made significant achievements in remote sensing object detection tasks. However, small weak objects located in complex scenes are still not effectively addressed. The lack of feature information and negligible contributions during the optimization stage are the main reasons. To solve the indicated issues, a novel remote-sensing object detection method is proposed in this letter. Firstly, the context feature integration module (CFIM) is designed to extract implicit clues co-occurring with the object to compensate for the lack of features in small weak objects. The receptive field expansive deformable convolution (RFConv) constructed in CFIM can adaptively adjust the information extraction range based on the object’s characteristics, thereby capturing suitable context features. Secondly, to make small weak objects competitive during the optimization process, we propose tailored optimization functions: the balanced sampling strategy (BSS) and the modulated loss function (MLF). BSS makes up for the sample deficiency of small weak objects by balancing sampling. More specifically, BSS dynamically mines potential positive samples from the ignored set in order to increase the chances of matching. MLF progressively adjusts the loss proportion of the object and pushes the detector to be more sensitive to small weak objects during the training stage. Our proposed method achieves 94.79% and 71.23% mAP on the NWPU VHR-10 and DIOR datasets, which also proves the effectiveness.