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Multi-instance mining with dynamic localization for weakly supervised object detection in remote-sensing images
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AI
DOI：10.1080/01431161.2025.2479886.png)
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Abstract
En 中文
Weakly supervised remote-sensing image object detection is crucial in the interpretation of remote-sensing images. Current mainstream approaches often focus on selecting the highest-scoring instances for training the detection network or overlook the issue of feature inconsistency within object classes during model training, leading to missed instances. Moreover, these methods typically use the same scheme for pseudo-label selection during training, which fails to achieve dynamic object localization, thus limiting the detection performance of the model. To address these challenges, we propose a feature-consistent and spatial voting multi-instance mining framework that also implements dynamic object localization. Specifically, we design a feature consistency learning module that inputs the same image at different angles into the Weakly Supervised Deep Detection Network (WSDDN) simultaneously, capturing objects of the same class from various angles by combining different detection results. Next, a Spatial Voting Instance Mining (SVIM) module is introduced to perform spatial voting by leveraging the scores and spatial relationships of instances. This module also applies boundary regularization to the voting results, effectively mining more reliable instances for supervision and addressing the issue of the detector focusing only on parts of the object. Finally, the Object Dynamic Localization (ODL) module uses stage prediction results as pseudo-label supplements during training, which greatly enhances the localization accuracy of the detector. Extensive experiments were carried out on the challenging Northwestern Polytechnic University (NWPU) VHR-10.v2 dataset and the Optical RSI (DIOR) dataset. The experimental results demonstrated that the proposed method can significantly improve detection accuracy.
Keywords:
Weakly supervised object detection
feature consistency
spatial voting
dynamic localization
remote-sensing images
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