The identification of sensing group targets and extended targets is of paramount importance in the context of vehicle tracking and early warning detection. As the scope of target monitoring and tracking extends, conventional single-sensor-based tracking techniques are proving to be inadequate in meeting the practical demands of the field. Consequently, multi-sensor fusion tracking technology has emerged as a viable alternative. However, the use of multiple sensors is constrained by their limited fields of view (FOVs), which leads to issues such as the loss of target detection and the introduction of false targets after fusion. Hence, this study proposes a combination of weighted geometric averaging (WGA) and weighted arithmetic averaging (WAA) methods to solve distributed multi-group/extended target tracking with different fields of view. Specifically, a local Poisson multi-Bernoulli mixture (PMBM) filter was first used on individual sensors. Subsequently, we combined a sequential fusion technique and the proposed fusion approach to fuse the PMBM filter densities. The efficacy and superiority of this approach were demonstrated through simulations.

Edge detection of space targets is vital in aerospace applications, such as satellite monitoring and analysis, yet it faces challenges due to diverse target shapes and complex backgrounds. While deep learning-based edge detection methods dominate due to their powerful feature representation capabilities, they often suffer from large parameter sizes and lack explicit geometric prior constraints for space targets. This paper proposes a novel edge detection method for satellite targets based on knowledge distillation, namely STED-KD. Firstly, a multi-stage distillation strategy is proposed to guide a lightweight, fully convolutional network with fewer parameters to learn key features and decision boundaries from a complex teacher model, achieving model efficiency. Next, a shape prior guidance module is integrated into the student branch, incorporating geometric shape information through shape prior model construction, similarity metric calculation, and feature reconstruction, enhancing adaptability to space targets and improving detection accuracy. Additionally, a curvature-guided edge loss function is designed to ensure continuous and complete edges, minimizing local discontinuities. Experimental results on the UESD space target dataset demonstrate superior performance, with ODS, OIS, and AP scores of 0.659, 0.715, and 0.596, respectively. On the BSDS500, STED-KD achieves ODS, OIS, and AP scores of 0.818, 0.829, and 0.850, respectively, demonstrating strong competitiveness and further confirming its stability.

To address severe fire safety risks caused by electric motorcycles (EMs) and their batteries being illegally brought into building elevators, this paper presents a real-time EM detection and alarm system for elevator environments, built upon a multi-source information fusion framework and an improved YOLOv9. To elevate detection accuracy for EMs in confined elevator spaces, two core optimizations are embedded into the network: the Programmable Gradient Information (PGI) training strategy, and a lightweight Generalized Efficient Layer Aggregation Network (GELAN) backbone enhanced with depthwise separable convolution (DSConv). A dedicated dataset consisting of roughly 2,000 images is established for model training and validation. This dataset covers a wide range of elevator scenes, diverse target postures, and common occlusion cases. Experimental results show that the proposed model achieves mAP@0.5 values of 0.952, 0.915 and 0.887 across three challenging elevator scenarios. The average per-frame inference latency is less than 35 ms, which fully meets the real-time constraints for edge device deployment. To construct a complete closed-loop detection and alarm system, we further design an alarm subsystem empowered by multi-source information fusion. It adopts dual-threshold decision logic and supports multimodal alerts including audio, visual cues, and log recording. This framework first judges the presence of EMs via a basic confidence threshold, then verifies the temporal continuity of detection results using a dedicated alarm threshold, and finally triggers multimodal alarms accordingly. This system effectively improves the efficiency of alarm notifications and provides a practical technical solution for fire safety management of elevators in residential and commercial buildings.

To address the safety hazards posed by unauthorized "black flight" of unmanned aerial vehicle (UAV) clusters, as well as the limitations of existing detection methods in tiny target recognition and complex environment adaptability, this paper proposes a real-time detection and counting method based on a feature fusion framework. The core contribution of this work is a novel multi-level fusion architecture that aggregates multi-scale visual features to enhance detection robustness under challenging conditions. First, we construct a multi scene UAV data set containing 7,113 images, and design a dedicated feature fusion module based on improved YOLOv9 backbone. The Haar Wavelet Down-sampling (HWD) module is introduced to fuse high-frequency edge details with low-frequency approximation features, preserving critical texture information that is essential for tiny target detection. The Programmable Gradient Information (PGI) mechanism is adopted as an auxiliary reversible branch, which propagates reliable deep-layer gradient information to shallow layers to effectively mitigate the information bottleneck problem. Second, a decision-level fusion strategy is developed to aggregate confidence scores and localization outputs from multi-scale predictions. The strategy is further optimized with a tailored Non-Maximum Suppression (NMS) algorithm to resolve occlusion issues in dense UAV clusters. Evaluations on the self-built dense UAV dataset show that the proposed method achieves a detection accuracy of 85% with a per-frame processing time of 0.10–0.15 s. Tests across diverse scenarios validate that the method can accurately locate and identify UAV targets. By achieving a favorable balance between detection accuracy and real-time performance, the proposed method effectively alleviates the missed detection and false detection problems in dense UAV cluster detection tasks.