Dynamic Cascade Detector for Storage Tanks and Ships in Optical Remote Sensing Images

Regional Convolutional Neural Network (RCNN)−based detectors have played a crucial role in object detection in remote sensing images due to their exceptional detection capabilities. Some studies have shown that different stages should have different Intersections of Union (IoU) thresholds to distinguish positive and negative samples because each stage has different IoU distributions. However, these studies have overlooked the fact that the IoU distribution at each stage changes continuously during the training process. Therefore, the IoU threshold at each stage should also be adjusted continuously to adapt to the changes in the IoU distribution. We realized that the IoU distribution at each stage is very similar to a Gaussian skewed distribution. In this paper, we introduce a novel dynamic IoU threshold method based on the Cascade RCNN architecture, called the Dynamic Cascade detector, with reference to the Gaussian skewed distribution. We tested the effectiveness of this method by detecting horizontal storage tanks and rotated ships in optical remote sensing images. Our experiments demonstrated that this technique can significantly improve detection results, as evaluated based on the COCO metric. In addition, the threshold range of the last stage impacts other stages, so the threshold range of one stage may change significantly when the number of stages changes. Furthermore, the threshold may not always increase during the training process and may decrease when the IoU distribution resembles a negatively skewed distribution.