Object detection is a fundamental task in computer vision. Recently, deep-learning-based object detection has made significant progress. However, due to large variations in target scale, the predominance of small targets, and complex backgrounds in remote sensing imagery, remote sensing object detection still faces challenges, including low detection accuracy, poor real-time performance, high missed detection rates, and high false detection rates in practical applications. To enhance remote sensing target detection performance, this study proposes a new model, the remote sensing detection transformer (RS-DETR). First, we incorporate cascaded group attention (CGA) into the attention-driven feature interaction module. By capturing features at different levels, it enhances the interaction between features through cascading and improves computational efficiency. Additionally, we propose an enhanced bidirectional feature pyramid network (EBiFPN) to facilitate multi-scale feature fusion. By integrating features across multiple scales, it improves object detection accuracy and robustness. Finally, we propose a novel bounding box regression loss function, Focaler-GIoU, which makes the model focus more on difficult samples, improving detection performance for small and overlapping targets. Experimental results on the satellite imagery multi-vehicles dataset (SIMD) and the high-resolution remote sensing object detection (TGRS-HRRSD) dataset show that the improved algorithm achieved mean average precision (mAP) of 78.2% and 91.6% at an intersection over union threshold of 0.5, respectively, which is an improvement of 2.0% and 1.5% over the baseline model. This result demonstrates the effectiveness and robustness of our proposed method for remote sensing image object detection.

In recent years, multi-task learning (MTL) has been shown to have considerable potential for enhancing model performance through the inter-task sharing of knowledge. Nevertheless, the effective balancing of optimization across diverse tasks remains a significant challenge. This paper presents a novel Deep Reinforcement Learning (DRL)-based framework that employs an Actor-Critic architecture to dynamically adjust task weights. In contrast to traditional methodologies that depend on heuristic rules or predefined assumptions about inter-task relationships, our approach autonomously learns optimal weighting strategies through interactions between an intelligent agent and the multi-task environment. The experimental results obtained across multiple datasets demonstrate that our method not only enhances overall performance and achieves a more balanced distribution of tasks but also maintains high training efficiency. Furthermore, the framework’s robust generalization and adaptability make it well-suited for addressing complex and dynamic learning scenarios.

The dominant low-light object detectors face the following spectral trilemma: (1) the loss of high-frequency structural details during denoising, (2) the amplification of low-frequency illumination distortion, and (3) cross-band interference in multi-scale features. To resolve these intertwined challenges, we present UMFNet—a frequency-guided detection framework that unifies adaptive frequency distillation with inter-band attention coordination. Our technical breakthroughs manifest through three key innovations: (1) a frequency-adaptive fusion (FAF) module employing learnable wavelet kernels (16–64 decomposition basis) with dynamic SNR-gated thresholding, achieving an 89.7% photon utilization rate in ≤1 lux conditions—2.4× higher than fixed-basis approaches; (2) a spatial-channel coordinated attention (SCCA) mechanism with dual-domain nonlinear gating that reduces high-frequency hallucination by 37% through parametric phase alignment (verified via gradient direction alignment coefficient ${\rho }_G$ = 0.93); (3) a spectral perception loss combining the frequency-weighted structural similarity index measure (SSIM) with gradient-aware focal modulation, enforcing physics-constrained feature recovery. Extensive validation demonstrates UMFNet’s leadership: 73.1% mAP@50 on EXDark (+6.4% over YOLOv8 baseline), 58.7% on DarkFace (+3.1% over GLARE), and 40.2% on thermal FLIR ADAS (+9.7% improvement). This work pioneers a new paradigm for precision-critical vision systems in photon-starved environments.

Object detection in remote sensing images has broad applications in military reconnaissance, urban planning, and disaster management. However, detecting small targets and extracting multi-scale features in complex scenes remain challenging. This paper presents YOLOv8s-Improved, enhancing small-target detection via PPHGNetV2, Progressive Feature Pyramid Network (AFPN-P2), and Diverse Branch Block (DBB) modules. Experiments on the DIOR and VisDrone2019 datasets show that YOLOv8s-Improved achieves mAP scores of 0.824 and 45.3%, respectively, representing improvements of 1.5 and 6.4 percentage points over the baseline YOLOv8s model (0.809 and 38.9%). The improved model demonstrates strong performance in multi-category object detection, particularly in complex scenes. The results suggest that the proposed method addresses the challenges of small target detection in remote sensing and exhibits generalization capabilities across different datasets.

The development of 5G environments has several advantages, including accelerated data transfer speeds, reduced latency, and improved energy efficiency. Nevertheless, it also increases the risk of severe cybersecurity issues, including a complex and enlarged attack surface, privacy concerns, and security threats to 5G core network functions. A 5G core network DDoS attack detection model is been proposed which utilizes a binary improved non-Bald Eagle optimization algorithm (Sin-Cos-bIAVOA) originally designed for IoT DDoS detection to select effective features for DDoS attacks. This approach employs a novel composite transfer function (Sin-Cos) to enhance exploration. The proposed method’s performance is compared with classical algorithms on the 5G Core PFCP DDoS attacks dataset. After rigorous testing across a spectrum of attack scenarios, the proposed detection model exhibits superior performance compared to traditional DDoS detection algorithms. This is a significant finding, as it suggests that the model achieves a higher degree of detection accuracy, meaning it is better equipped to identify and mitigate DDoS attacks. This is particularly noteworthy in the context of 5G core networks, as it offers a novel solution to the problem of DDoS attack detection for this critical infrastructure.

Smart library services increasingly depend on heterogeneous IoT terminals and edge nodes, whose computing resources must be scheduled under three conflicting objectives—latency, energy, and cost—with a trade-off that shifts with the business cycle. Existing deep-reinforcement-learning (DRL) schedulers mostly scalarize these objectives with fixed weights and therefore cannot re-balance the trade-off as the business context changes. We propose BC-PA-DDQN, a business-cycle-aware preference-adaptive dueling double DQN that conditions the Q-value on a task-level preference vector: each task’s weights combine a task-type prior, a business-period modulation, and a per-task log-normal perturbation, replacing the fixed uniform preference of the baseline PA-DDQN. Because no public library-computing dataset with per-task telemetry exists, we calibrate the business-cycle arrival multipliers and task-type composition from 18 months of real library operational data and model the task-size and SLA distributions from domain knowledge. Over 10 seeds with per-seed paired sign-permutation tests, BC-PA-DDQN significantly outperforms the fixed-preference PA-DDQN ($+0.984$ reward, Holm-corrected $p=0.0234$, 10/10 seeds) and the DDQN/D3QN/greedy/random baselines, and it shows no significant difference from a maximum-entropy SAC baseline ($p=0.56$). A factor test and a leave-one-phase-out test show that the numerical preference carries information beyond the phase label and transfers to unseen phases. The gain is an explicit energy-for-cost trade-off, and the 5316-parameter network is about 3× smaller than SAC.