Capsule-inferenced Object Detection for Remote Sensing Images

Abstract

Frequent and accurate object detection based on remote sensing images can effectively monitor dynamic objects on the Earth's surface. While the Detection Transformer (DETR) offers a simple encoder-decoder structure and a direct set prediction approach to object detection, it falls short in complex remote sensing scenes where entity information and relative positions between objects are critical to target reasoning. Notably, the DETR model's feedforward neural network (FFN) relies on weighted summation for target reasoning, disregarding interactive feature information, which is a major factor affecting detection effectiveness. To address these shortcomings, we propose a DETR-based detection model called (CI_DETR), which uses capsule inference to improve remote sensing object detection. Our approach adds a Multi-level Feature Fusion module to the DETR network, allowing the network to learn how to spatially alter features at different levels, preserving only beneficial information for combination. In addition, we introduce a capsule reasoning module to mine entity information during inference and more effectively model the hierarchical correlation of internal knowledge representation in the neural network, consistent with the thinking model of the human brain. Lastly, we employ a sausage model to measure the similarities and differences of capsules, projecting them onto a curved surface for nonlinear function approximation and maximum preservation of the local responsiveness of capsule entities. Our experiments on public datasets confirm the superior detection performance of our proposed algorithm relative to many current detectors.