Progress of research on deep learning algorithms for object detection in optical remote sensing images

Among all applications of optical remote sensing images, object detection has always been given more attention by researchers. Object detection has a wide application prospect in military and civilian fields. This study reviews the progress of research on object detection algorithms in optical remote sensing images on the basis of deep learning. The characteristics of remote sensing objects are different from those of conventional objects. First, remote sensing equipment has a long imaging distance, so it can cover a large range. The images may have objects with large scale and shape changes. Second, in remote sensing images, the background tends to occupy a large area. As a result, some objects are often submerged in the complex background, and detectors cannot distinguish these objects effectively. Last, in remote sensing images, the objects do not only have a small size and changeable direction. Sometimes, remote sensing objects are densely distributed, posing challenges to the detection of optical remote sensing objects. This study introduces the development process of optical remote sensing object detection algorithms from template matching, prior knowledge, and machine learning to deep learning. Then, the process of optical remote sensing object detection based on deep learning, including data preprocessing, feature extraction, detection, and postprocessing, is introduced in detail. Classical deep learning-based object detection algorithms, including the one-stage algorithms represented by YOLO and SSD and the two-stage algorithm represented by Faster RCNN, are summarized. Afterward, in accordance with the characteristics of optical remote sensing image objects, various improved algorithms for addressing the optical remote sensing image object detection problems of scale diversity, direction diversity, shape diversity, small size, feature similarity, background complexity, distribution density, and weak features are systematically summarized. Non-strong supervised learning-based optical remote sensing image object detection methods and other advanced algorithms, such as Transformer-based algorithms, transfer learning-based algorithms, knowledge graph-based algorithms, and prior knowledge-based algorithms, are also summarized. In addition, open-source optical remote sensing image datasets and the performance of object detection evaluation indexes are introduced. The Mean Average Precision (mAP) of advanced algorithms on the NWPU-VHR10 dataset can exceed 90%. On the DOTA dataset, the mAP of each advanced algorithm decreases considerably, and the advanced algorithms proposed in recent years continue to improve their performance on this dataset. Multiscale fusion with a feature pyramid network has become the mainstream method of advanced algorithms, which can detect multiscale objects effectively. Many improved algorithms have been proposed to solve the abovementioned problems in optical remote sensing image object detection, and good detection results have been achieved. However, the research on object detection in large-scale remote sensing images and similar object detection between classes remains lacking. Furthermore, this study proposes future development directions, such as improved deep learning networks, lightweight networks, weakly supervised learning, small-object detection, and improved rotary detection mechanisms. © 2024 Science Press. All rights reserved.