Wei Tang

Multimodal remote sensing data provide important data support for large-scale, all-weather monitoring of offshore oil and gas platforms. However, differences in imaging modalities and acquisition conditions introduce significant domain shift, which limits the generalization capability of object detection models in unseen target domains. Furthermore, most existing multimodal detection methods typically rely on paired and strictly registered data, thereby restricting their applicability in practical scenarios. To overcome these challenges, an end-to-end method, termed OG-MLDD, is proposed for cross-modal offshore oil and gas platform detection. Without requiring multimodal image registration, the proposed method integrates meta-learning with domain discriminator (DD) to encourage the extraction of semantically discriminative representations that remain invariant across different domains. Specifically, a dual-gradient-descent-based meta-learning strategy is first introduced to jointly optimize the meta-train and meta-test objectives, encouraging the model to learn generalized and discriminative feature representations across different imaging modalities. Subsequently, a dynamic-weighted gradient reversal layer (DWGRL) is embedded into DD to guide adversarial feature learning between different domains, thereby promoting the acquisition of domain-robust representations and supplying global supervisory cues throughout the training process. Finally, a multi-scale feature aggregation (MFA) module is proposed to effectively integrate fine-grained local details with multiple receptive fields and high-level contextual information, thereby refining the fused feature representations and enhancing the representation capability of multi-scale offshore platform objects. Experimental results on a real-world dataset demonstrate that, even without using target domain data during training, the proposed method achieves superior detection performance and robust cross-domain generalization. Compared with existing methods, the full method improves detection accuracy by approximately 8 percentage points with a standard deviation of only 0.6, highlighting its superior detection performance and generalization ability.