Dynamic Sensing and Correlation Loss Detector for Small Object Detection in Remote Sensing Images

Recently, significant object detection achievements have emerged for optical remote sensing images. However, the performance and efficiency of small object detection are still highly unsatisfactory because of the scale diversity between the objects; furthermore, small objects always have small amounts of effective information, which are difficult to locate. To address this problem, we propose a novel dynamic sensing and correlation loss detector (DCDet) for performing object detection in remote sensing images. The detector consists of two modules: a small object dynamic sensing (SODS) module and a simple but effective correlation loss (CrLoss) function. SODS is utilized to capture the information of small objects in a scale sequence. We consider the feature pyramid as a set of video frames when the camera is zoomed in on the image and use the object focusing module in dynamic sensing to always focus on the small objects in each video frame. The detection performance achieved for small objects is improved by shifting the detector’s attention from the entire image to small objects within the frame to provide a multiscale feature representation of the small objects and their contextual information. The CrLoss is a special CrLoss for remote sensing image object detection tasks and directly optimizes the correlation coefficient to improve the performance of a detector. Extensive experiments conducted on the publicly available DOTA, DIOR-R, and HRSC2016 datasets show that our DCDet outperforms the existing state-of-the-art remote sensing object detection methods in terms of many evaluation metrics.