There are differences in imaging characteristics between infrared and visible light images: visible light images can provide rich texture and color information, but imaging is limited in harsh weather conditions. Infrared images are based on the target’s thermal radiation characteristics and have the ability to resist environmental interference but lack details and background information. Effectively integrating the two can significantly enhance scene understanding ability and improve environmental perception and target recognition performance in applications such as intelligent driving. However, existing fusion methods still face challenges, especially in complex scenes where it is difficult to balance the full preservation of target information with the complete presentation of background details, often resulting in difficulties in extracting differentiated features from different modalities. This article proposes a target detection method based on the visible light infrared fusion diffusion model. This method introduces the Stable Diffusion architecture and designs a target perception spatial fusion weight module that can adaptively generate a spatial fusion weight map based on modal differences. By implementing a multi-stage dynamic fusion strategy, the fusion ratio is automatically adjusted at different diffusion stages. A full-step multi-step prediction mechanism is adopted to improve fusion quality and stability. Compared with existing methods, the method proposed in this article has significant advantages. Experiments on multiple publicly available datasets have shown that this method outperforms existing mainstream methods in key metrics such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and ean Absolute Error (MAE) and also demonstrates good detection performance in downstream tasks for object detection.

In the field of low-altitude aerial drone data fusion, the fusion of infrared and visible light images remains challenging due to issues such as large modal differences, insufficient cross-modal alignment, and limited global context modeling. Traditional methods struggle to extract complementary information across modalities, while deep learning methods often lack sufficient global receptive fields (convolutional neural networks) or fail to preserve local details (standard Transformers). To address these issues, we propose a Cross-modal Guided Dual-Branch Network (CGDBN) that combines convolutional neural networks and Transformer architecture. Our framework contribution: We designed a Target-modal Feature Extraction Mechanism (TMFEM) module with specialized thermal characteristics for infrared feature extraction, which does not require processing of visible light features; we introduced Simplified Linear Attention Blocks (SLABs) into our framework to improve global context capture as a module; we designed a Cross-Modal Interaction Mechanism (CMIM) module for bidirectional feature interaction; and we designed a Density Adaptive Multimodal Fusion (DAMF) module that weights modal contributions based on content analysis. This asymmetric design recognizes that different types of images have different characteristics and require targeted processing. The experimental results on AVMS, M3FD, and TNO datasets show that the proposed model has a peak signal-to-noise ratio (PSNR) of 16.2497 on the AVMS dataset, which is 0.9971 higher than the best benchmark method YDTR (peak signal-to-noise ratio: approximately 15.2526). The peak signal-to-noise ratio on the M3FD dataset is 16.5044, which is 0.7480 higher than the best benchmark method YDTR (peak signal-to-noise ratio of approximately 15.7564). The peak signal-to-noise ratio on the TNO dataset is 17.3956, which is 0.7934 higher than the best benchmark method YDTR (peak signal-to-noise ratio: approximately 16.6022), and the overall performance on all other indicators is among the top in all comparison models. This method has broad application prospects in fields such as drone data fusion.