Weakly Supervised Part-Based Method for Combined Object Detection in Remote Sensing Imagery

Abstract

Deep learning methods have reached considerable achievement on remote sensing object detection in recent years. However, most methods are designed for single object detection, such as vehicles and ships, and have limited detection capabilities for the combined object with large scale and complex part structure. In this paper, we propose a Part-based Topology Distillation Network (PTDNet) for accurate and efficient combined object detection in remote sensing imagery. Specifically, a Part-based Feature Module (PFM) is designed to extract the key parts information of combined object in a weakly supervised manner. Besides, to balance the accuracy and efficiency of the model, with considering the topology structure of multiple parts in combined objects, a lightweight network training method based on partial topological feature distillation is proposed to improve the model performance without additional parameters. Experiments show that the PTDNet outperforms the state-of-the-art methods and achieves 65.4% mAP and 84.1% accuracy for combined object detection.

Weakly Supervised Object Detection for Remote Sensing Images via Progressive Image-Level and Instance-Level Feature Refinement

“…According to [32][33][34][35], WSOD in RSIs still suffers from two major challenges: part domination and missing detection. Qian et al [36] constructed a part-based topology distillation network (PTDNet) to perceive the combined instances via the extracted most informative parts of objects. Layer-wise relevance propagation (LRP) and point set representation (RepPoints) [37] were proposed to reduce the ambiguities in object recognition.…”