UAV multitarget detection plays a pivotal role in civil and military fields. Although deep learning methods provide a more effective solution to this task, changes in target size, shape change, occlusion, and lighting conditions from the perspective of drones still bring great challenges to research in this field. Based on the above problems, this paper proposes an aerial image detection model with excellent performance and strong robustness. First, in view of the common problem that small targets in aerial images are prone to misdetection and missed detection, the idea of Bi-PAN-FPN is introduced to improve the neck part in YOLOv8-s. By fully considering and reusing multiscale features, a more advanced and complete feature fusion process is achieved while maintaining the parameter cost as much as possible. Second, the GhostblockV2 structure is used in the backbone of the benchmark model to replace part of the C2f module, which suppresses information loss during long-distance feature transmission while significantly reducing the number of model parameters; finally, WiseIoU loss is used as bounding box regression loss, combined with a dynamic nonmonotonic focusing mechanism, and the quality of anchor boxes is evaluated by using “outlier” so that the detector takes into account different quality anchor boxes to improve the overall performance of the detection task. The algorithm’s performance is compared and evaluated on the VisDrone2019 dataset, which is widely used worldwide, and a detailed ablation experiment, contrast experiment, interpretability experiment, and self-built dataset experiment are designed to verify the effectiveness and feasibility of the proposed model. The results show that the proposed aerial image detection model has achieved obvious results and advantages in various experiments, which provides a new idea for the deployment of deep learning in the field of UAV multitarget detection.

Numerical optimization, Unmanned Aerial Vehicle (UAV) path planning, and engineering design problems are fundamental to the development of artificial intelligence. Traditional methods show limitations in dealing with these complex nonlinear models. To address these challenges, the swarm intelligence algorithm is introduced as a metaheuristic method and effectively implemented. However, existing technology exhibits drawbacks such as slow convergence speed, low precision, and poor robustness. In this paper, we propose a novel metaheuristic approach called the Red-billed Blue Magpie Optimizer (RBMO), inspired by the cooperative and efficient predation behaviors of red-billed blue magpies. The mathematical model of RBMO was established by simulating the searching, chasing, attacking prey, and food storage behaviors of the red-billed blue magpie. To demonstrate RBMO’s performance, we first conduct qualitative analyses through convergence behavior experiments. Next, RBMO’s numerical optimization capabilities are substantiated using CEC2014 (Dim = 10, 30, 50, and 100) and CEC2017 (Dim = 10, 30, 50, and 100) suites, consistently achieving the best Friedman mean rank. In UAV path planning applications (two-dimensional and three − dimensional), RBMO obtains preferable solutions, demonstrating its effectiveness in solving NP-hard problems. Additionally, in five engineering design problems, RBMO consistently yields the minimum cost, showcasing its advantage in practical problem-solving. We compare our experimental results with three categories of widely recognized algorithms: (1) advanced variants, (2) recently proposed algorithms, and (3) high-performance optimizers, including CEC winners.

Colloidal assembly into chiral superstructures is usually accomplished with templating or lithographic patterning methods that are only applicable to materials with specific compositions and morphologies over narrow size ranges. Here, chiral superstructures can be rapidly formed by magnetically assembling materials of any chemical compositions at all scales, from molecules to nano- and microstructures. We show that a quadrupole field chirality is generated by permanent magnets caused by consistent field rotation in space. Applying the chiral field to magnetic nanoparticles produces long-range chiral superstructures controlled by field strength at the samples and orientation of the magnets. Transferring the chirality to any achiral molecules is enabled by incorporating guest molecules such as metals, polymers, oxides, semiconductors, dyes, and fluorophores into the magnetic nanostructures.