(1) Objective: Remote sensing object detection faces significant challenges, including complex background interference, large variations in target scales, and insufficient multi-scale feature representation, which often result in missed detections of small objects, inaccurate localization, and inadequate feature fusion. (2) Methods: To address these issues, this paper proposes a Multi-scale Attention Enhancement Feature Fusion Network (MAEF-Net) based on YOLOv12. The proposed method systematically optimizes the overall detection pipeline from three aspects, namely feature enhancement, feature fusion, and prediction refinement. Specifically, it enhances local texture representation and high-level semantic information in the backbone, improves multi-scale feature interaction during the feature fusion stage, and adaptively filters fused features in the detection stage to strengthen target responses while suppressing background noise, thereby improving the detection performance for complex scenes, small objects, and densely distributed targets. (3) Results: To validate the effectiveness of the proposed method, extensive experiments are conducted on two public remote sensing object detection datasets, namely RSOD and NWPU VHR-10. Experimental results demonstrate that, compared with the baseline YOLOv12, MAEF-Net improves the mAP by 1.86% and 1.05% on the RSOD and NWPU VHR-10 datasets, respectively. Furthermore, compared with the latest YOLOv13, the proposed method achieves additional mAP improvements of 1.53% and 0.43%, respectively. Moreover, MAEF-Net achieves a favorable balance between detection accuracy and computational efficiency while maintaining relatively low computational complexity and high inference speed. Ablation studies, comparative experiments, and qualitative visualization further demonstrate the effectiveness, robustness, and generalization capability of the proposed method in complex remote sensing scenarios.
Accurate prediction of computing-resource workloads is important for capacity planning, overload warning, and intelligent system management. Large-scale computing systems exhibit complex temporal fluctuations, sudden variations, and multi-resource coupling characteristics, making accurate workload prediction challenging. To address these challenges, this paper proposes a Hybrid Temporal-Feature Attention enhanced Multi-task Stacking model (HTAM-Stack) for multivariate cluster workload forecasting. First, a Temporal-Feature Hybrid Attention (TFHA) module is designed to jointly capture temporal dependencies and cross-resource feature interactions, enabling adaptive extraction of critical temporal patterns and important resource characteristics. Second, a Multi-Task Learning (MTL) framework is introduced to simultaneously predict CPU and Memory workloads by exploiting the correlations among heterogeneous resource variables. Furthermore, a Dynamic Stacking (DS) mechanism is developed to adaptively adjust the contributions of heterogeneous base learners through a weight generation network, and a Residual Corrector (RC) is incorporated to further enhance prediction robustness. Extensive experiments conducted on two widely used public cluster workload datasets, including Google Cluster Trace and Alibaba Cluster Trace, demonstrate that HTAM-Stack achieves competitive prediction performance under complex and dynamic workload conditions. The proposed model achieves MAE values of 0.0012 and 0.0010, RMSE values of 0.0031 and 0.0027, MAPE values of 0.20% and 0.16%, and R2 values of 0.9715 and 0.9782 on the two datasets, respectively. Moreover, HTAM-Stack requires only 3.20 ms inference time with 8.60 M parameters, achieving a favorable balance between prediction accuracy and computational efficiency. The results validate the general effectiveness of the proposed framework on public cluster workload benchmarks rather than its direct applicability to railway IT systems. Because no representative railway workload dataset was available, railway IT is discussed only as a potential application context that requires domain-specific validation.
