Real-time weapon detection is a critical component of intelligent surveillance systems, particularly for perimeter monitoring applications on embedded edge platforms. However, reliable alarm generation remains challenging because false positives, temporal instability, and viewpoint inconsistencies can propagate through conventional multi-camera fusion strategies. To address these limitations, this work proposes a lightweight Adaptive Multi-Camera Temporal Fusion (ACTF) framework that combines confidence-aware evidence separation, temporal persistence, and short-window cross-camera validation at the decision level, thereby confirming detections without requiring additional neural-network inference. The framework was evaluated using a TensorRT-optimized YOLO26s detector in controlled dual-camera scenarios involving clear visibility, partial occlusion, visually ambiguous distractors, and challenging illumination. While logical OR fusion achieved higher recall, it also propagated erroneous detections; in contrast, ACTF completely suppressed the distractor-induced false alarms while maintaining competitive performance and sub-second confirmation under favorable conditions. The original NVIDIA Jetson Nano implementation achieved an average throughput of 2.4 camera-pair cycles per second, corresponding to low-rate online embedded operation, whereas an additional NVIDIA Jetson Xavier NX benchmark achieved an average of 10.2 camera-pair cycles per second. This is equivalent to 10.2 processed frames per second for each camera stream and 20.4 camera images per second in aggregate. These results support reactive real-time embedded operation on the Xavier NX platform and demonstrate that ACTF improves alarm reliability with negligible decision-level computational overhead.
Carlos Julio Fierro-Silva, Carolina Del-Valle-Soto, S. M. Mostafa et al.· IEEE Access· 0 citations
Real-time weapon detection in video surveillance systems is a critical requirement for proactive security applications, particularly under the computational and latency constraints imposed by edge artificial intelligence deployments. While the YOLO family of object detectors has undergone continuous architectural evolution, the recently introduced YOLOv26 represents a significant redesign aimed at improving efficiency, stability, and deployment suitability across a wide range of hardware platforms. This work presents a comprehensive and homogeneous experimental evaluation of the full YOLOv26 model family, ranging from nano (YOLOv26n) to extra-large (YOLOv26x) variants, for real-time weapon detection in surveillance imagery. All models are trained and evaluated under identical conditions using a dataset that explicitly includes visually similar non-weapon objects as hard negatives, enabling a realistic assessment of false positives and false negatives in safety critical scenarios. The analysis encompasses training and validation dynamics, precision, recall evolution, mean Average Precision (mAP) at multiple IoU thresholds, class-wise confusion matrices, and inference latency. Results show that performance improves consistently from smaller to medium sized models, with YOLOv26m achieving the most balanced trade off between detection accuracy, robustness, and computational cost. Larger variants provide marginal accuracy gains at significantly higher complexity, revealing diminishing returns for edge oriented deployments. Overall, the findings demonstrate that the YOLOv26 architecture offers a scalable and mature detection framework, where model selection can be guided by explicit operational criteria rather than raw accuracy alone. This study establishes a strong baseline for future work on real world edge deployment, multi camera surveillance systems, and hardware aware optimization of next generation YOLO detectors.
Carlos Silva, Carolina Del-Valle-Soto, J. Varela-Aldás· 2026 6th International Confe...· 0 citations
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