Interest in anti-UAV detection systems has increased due to growing concerns about the security and privacy issues associated with unmanned aerial vehicles (UAVs). Achieving real-time detection with high accuracy, while accommodating the limited resources of edge-computing devices poses a significant challenge for anti-UAV detection. Existing deep learning-based models for anti-UAV detection often cannot balance accuracy, processing speed, model size, and computational efficiency. To address these limitations, a lightweight and efficient anti-UAV detection model, DRBD-YOLOv8, is proposed in this paper. The model integrates several innovations, including the application of a Re-parameterization Cross-Stage Efficient Layered Attention Network (RCELAN) and a Bidirectional Feature Pyramid Network (BiFPN), to enhance feature processing capabilities while maintaining a lightweight design. Furthermore, DN-ShapeIoU, a novel loss function, has been established to enhance detection accuracy, and depthwise separable convolutions have been included to decrease computational complexity. The experimental results showed that the proposed model outperformed YOLOV8n in terms of mAP50, mAP95, precision, and FPS while reducing GFLOPs and parameter count. The DRBD-YOLOv8 model is almost half the size of the YOLOv8n model, measuring 3.25 M. Its small size, fast speed, and high accuracy combine to provide a lightweight, accurate device that is excellent for real-time anti-UAV detection on edge-computing devices.

Multiobjective clustering algorithm using particle swarm optimization has been applied successfully in some applications. However, existing algorithms are implemented on a single machine and cannot be directly parallelized on a cluster, which makes it difficult for existing algorithms to handle large-scale data. With the development of distributed parallel computing framework, data parallelism was proposed. However, the increase in parallelism will lead to the problem of unbalanced data distribution affecting the clustering effect. In this paper, we propose a parallel multiobjective PSO weighted average clustering algorithm based on apache Spark (Spark-MOPSO-Avg). First, the entire data set is divided into multiple partitions and cached in memory using the distributed parallel and memory-based computing of Apache Spark. The local fitness value of the particle is calculated in parallel according to the data in the partition. After the calculation is completed, only particle information is transmitted, and there is no need to transmit a large number of data objects between each node, reducing the communication of data in the network and thus effectively reducing the algorithm’s running time. Second, a weighted average calculation of the local fitness values is performed to improve the problem of unbalanced data distribution affecting the results. Experimental results show that the Spark-MOPSO-Avg algorithm achieves lower information loss under data parallelism, losing about 1% to 9% accuracy, but can effectively reduce the algorithm time overhead. It shows good execution efficiency and parallel computing capability under the Spark distributed cluster.

The rapid expansion of nuclear technology across various sectors due to global economic growth has led to a substantial rise in the transportation of radioactive materials. The International Atomic Energy Agency (IAEA) estimates that approximately 20 million shipments of radioactive materials occur annually. In this context, ensuring the safety and security of radioactive material transportation is of significant importance. IAEA’s “Security of Radioactive Materials in Transport” (Nuclear Security Series No. 9-G) mandates that an effective transport security system should provide immediate detection of any unauthorized removal of the packages. In the present study, an innovative Adam-optimized BP neural network model is developed for detecting unauthorized movements of radioactive material packages. To analyze the performance of the proposed algorithm, numerous experiments were conducted. The results demonstrate that the proposed method achieves a 99.17% accuracy rate in detecting unauthorized movements of radioactive materials, with a missed alarm rate of 0.72% and a false alarm rate of 0.1%. This method also enables real-time detection of unauthorized removal of radioactive materials and effectively enhances the security of radioactive materials during transport to reduce the risks of theft, loss, diversion, or sabotage.

The loss and theft of radioactive material in transport can be attributed to the illegal movement. In order to distinguish it from the movement caused by the turbulence of the transportation vehicle, this paper proposes the criterion of “illegal movement” as the movement of radioactive materials outside the transportation compartment. Since the interior of the compartment is generally a metal environment, this paper proposes wireless signal strength data as a sensing method. The wireless signal strength data is filtered and converted into distance data. We construct a spatial triangle perpendicular to the top and sides of the compartment based on the distance data. When the radioactive material is inside the compartment, the angle between its corresponding point and the top plane of the compartment is less than 90°. Once it moves out of the compartment, the angle will be greater than 90°. Based on this, a sensing method of “illegal movement” based on spatial triangles is proposed. The simulation research shows that the scheme proposed in this paper is feasible.