Object detection in remote sensing scenarios plays an indispensable and significant role in civilian, commercial, and military areas, leveraging the power of convolutional neural networks (CNNs). Remote sensing images, captured by crafts and satellites, exhibit unique characteristics including complicated backgrounds, limited features, distinct density, and varied scales. The contextual and comprehensive information in an image can make a detector precisely localize and classify targets, which is extremely valuable for object detection in remote sensing scenarios. However, CNNs, restricted by the essence of the convolution operation, possess local receptive fields and scarce contextual information, even in large models. To address this limitation and improve detection performance by extracting global contextual information, we propose a novel plug-and-play attention module, named Convolution with Transformer Attention Module (CTAM). CTAM is composed of a convolutional bottleneck block and a simplified Transformer layer, which can facilitate the integration of local features and position information with long-range dependency. YOLOv8n, a superior and faster variant of the YOLO series, is selected as the baseline. To demonstrate the effectiveness and efficiency of CTAM, we incorporated CTAM into YOLOv8n and conducted extensive experiments on the DIOR dataset. YOLOv8n-CTAM achieves an impressive 54.2 mAP@50-95, surpassing YOLOv8n (51.4) by a large margin. Notably, it outperforms the baseline by 2.7 mAP@70 and 4.4 mAP@90, showcasing its superiority with stricter IoU thresholds. Furthermore, the experiments conducted on the TGRS-HRRSD dataset validate the excellent generalization ability of CTAM.

Infrared and visible image fusion aims to integrate complementary thermal target information and visible structural details for multimodal perception and remote sensing applications. However, existing deep learning methods often underutilize semantic information during fusion and mainly perform cross-modal interaction in the spatial domain, limiting the exploitation of complementary representations. To address these issues, we propose SDFNet, a semantic driven frequency-domain infrared and visible image fusion network based on an encoder–decoder architecture. Guided by semantic priors extracted from a pre-trained segmentation model, SDFNet introduces a frequency-domain feature aggregation module to enhance cross-modal feature interaction and semantic information preservation. Extensive experiments on the MSRS dataset demonstrate the effectiveness of the proposed method. On the 361-pair MSRS test set, SDFNet ranks among the top two on five of the six evaluated fusion metrics, achieving EN, AG, SF, SCD, and Qabf scores of 6.73, 3.78, 11.59, 1.74, and 0.69, respectively, while obtaining a competitive VIF score of 1.02. In the downstream semantic utility evaluation, SDFNet achieves the highest mIoU of 78.76 among the compared methods, further demonstrating its ability to preserve task-relevant semantic information.