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.

This paper aims to achieve real-time and accurate detection of surface defects by using a deep learning method. For this purpose, the Single Shot MultiBox Detector (SSD) network was adopted as the meta structure and combined with the base convolution neural network (CNN) MobileNet into the MobileNet-SSD. Then, a detection method for surface defects was proposed based on the MobileNet-SSD. Specifically, the structure of the SSD was optimized without sacrificing its accuracy, and the network structure and parameters were adjusted to streamline the detection model. The proposed method was applied to the detection of typical defects like breaches, dents, burrs and abrasions on the sealing surface of a container in the filling line. The results show that our method can automatically detect surface defects more accurately and rapidly than lightweight network methods and traditional machine learning methods. The research results shed new light on defect detection in actual industrial scenarios.

Numerical optimization and point cloud registration are critical research topics in the field of artificial intelligence. The differential evolution algorithm is an effective approach to address these problems, and LSHADE-SPACMA, the winning algorithm of CEC2017, is a competitive differential evolution variant. However, LSHADE-SPACMA’s local exploitation capability can sometimes be insufficient when handling these challenges. Therefore, in this work, we propose a modified version of LSHADE-SPACMA (mLSHADE-SPACMA) for numerical optimization and point cloud registration. Compared to the original approach, this work presents three main innovations. First, we present a precise elimination and generation mechanism to enhance the algorithm’s local exploitation ability. Second, we introduce a mutation strategy based on a modified semi-parametric adaptive strategy and rank-based selective pressure, which improves the algorithm’s evolutionary direction. Third, we propose an elite-based external archiving mechanism, which ensures the diversity of the external population and can accelerate the algorithm’s convergence progress. Additionally, we utilize the CEC2014 (Dim = 10, 30, 50, 100) and CEC2017 (Dim = 10, 30, 50, 100) test suites for numerical optimization experiments, comparing our approach against: (1) 10 recent CEC winner algorithms, including LSHADE, EBOwithCMAR, jSO, LSHADE-cnEpSin, HSES, LSHADE-RSP, ELSHADE-SPACMA, EA4eig, L-SRTDE, and LSHADE-SPACMA; (2) 4 advanced variants: APSM-jSO, LensOBLDE, ACD-DE, and MIDE. The results of the Wilcoxon signed-rank test and Friedman mean rank test demonstrate that mLSHADE-SPACMA not only outperforms the original LSHADE-SPACMA but also surpasses other high-performance optimizers, except that it is inferior L-SRTDE on CEC2017. Finally, 25 point cloud registration cases from the Fast Global Registration dataset are applied for simulation analysis to demonstrate the potential of the developed mLSHADE-SPACMA technique for solving practical optimization problems. The code is available at https://github.com/ShengweiFu?tab=repositories and https://ww2.mathworks.cn/matlabcentral/fileexchange/my-file-exchange