Remote sensing target detection faces significant challenges in handling multi-scale targets, with the high similarity in color and shape between targets and backgrounds in complex scenes further complicating the detection task. To address this challenge, we propose a multi-Scale and complex Background Aware network for remote sensing target detection, named YOLO-SBA. Our proposed YOLO-SBA first processes the input through the Multi-Branch Attention Feature Fusion Module (MBAFF) to extract global contextual dependencies and local detail features. It then integrates these features using the Bilateral Attention Feature Mixer (BAFM) for efficient fusion, enhancing the saliency of multi-scale target features to tackle target scale variations. Next, we utilize the Gated Multi-scale Attention Pyramid (GMAP) to perform channel–spatial dual reconstruction and gating fusion encoding on multi-scale feature maps. This enhances target features while finely suppressing spectral redundancy. Additionally, to prevent the loss of effective information extracted by key modules during inference, we improve the downsampling method using Asymmetric Dynamic Downsampling (ADDown), maximizing the retention of image detail information. We achieve the best performance on the DIOR, DOTA, and RSOD datasets. On the DIOR dataset, YOLO-SBA improves mAP by 16.6% and single-category detection AP by 0.8–23.8% compared to the existing state-of-the-art algorithm.

In open and dynamic environments, object detection is affected by rain, fog, snow, and complex lighting conditions, leading to decreased accuracy and posing a threat to driving safety. Infrared images can provide clear images at nighttime or in adverse weather conditions. Combined with the mature development of existing cross-modality object detection technologies, both of them offer support for addressing object detection issues in adverse weather scenarios. This paper establishes a novel dataset named Adverse Weather and Illumination Dataset (AWID) to simulate intricate real-world scenarios and proposes a cross-modal object detection algorithm for adverse weather scenarios in autonomous driving, named CME-YOLO, which is based on RGB and infrared images. It integrates the Cross-Perception Transformer Fusion algorithm, CPTFusion, and the Adaptive upsampling technique, AdSample, to enhance the extraction of detailed information and supplement effective information. CPTFusion fuses features from different modalities through multi-scale feature extraction and optimal fusion strategy computation. AdSample adaptively improves the utilization of key features and the quality of the resulting feature tensor. Experiments on two public datasets and AWID show that CME-YOLO performs optimally, with an mAP50 value on the FLIR dataset 6.8% higher than the state-of-the-art MPFT algorithm, verifying its excellent performance in autonomous driving object detection tasks.

In complex networks, the identification of critical nodes is vital for optimizing information dissemination. Given the significant role of these nodes in network structures, researchers have proposed various identification methods. In recent years, deep learning has emerged as a promising approach for identifying key nodes in networks. However, existing algorithms fail to effectively integrate local and global structural information, leading to incomplete and limited network understanding. To overcome this limitation, we introduce a transformer framework with multi-scale feature fusion (MSF-Former). In this framework, we construct local and global feature maps for nodes and use them as input. Through the transformer module, node information is effectively aggregated, thereby improving the model’s ability to recognize key nodes. We perform evaluations using six real-world and three synthetic network datasets, comparing our method against multiple baselines using the SIR model to validate its effectiveness. Experimental analysis confirms that MSF-Former achieves consistently high accuracy in the identification of influential nodes across real-world and synthetic networks.