Fierro Silva, Carlos Julio

Preferred name

Main Affiliation

Centro de investigación en Mecatrónica y Sistemas Interactivos

Ambato

Scopus Author ID

60604170700

Now showing 1 - 5 of 5

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A Multi-Camera Temporal Fusion for False Alarm Suppression in Edge-Based Weapon Surveillance
(Institute of Electrical and Electronics Engineers (IEEE), 2026)
Fierro Silva, Carlos Julio
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Del-Valle-Soto, Carolina
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Mostafa, Samih M.
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Karim, Faten Khalid
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Varela Aldas, José
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. © 2013 IEEE.
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Comparative analysis of previous YOLO detectors and YOLOv26s for real-time weapon detection in video surveillance
(Frontiers Media SA, 2026-04-28)
Fierro Silva, Carlos Julio
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Del-Valle-Soto, Carolina
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Bran, Carlos
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Varela Aldas, José
Real-time weapon detection in video surveillance is a critical capability for artificial intelligence assisted security systems, particularly in scenarios constrained by low latency, limited computational resources, and strict power efficiency requirements typical of edge artificial intelligence deployments. This work presents a comparative analysis of lightweight YOLO based object detectors, namely YOLOv8s, YOLOv9s, YOLOv10s, and YOLOv11s, vs. the recently introduced YOLOv26s model. In contrast to conventional benchmarking studies, this work extends the evaluation to real-world edge deployment conditions using an NVIDIA Jetson Nano device, explicitly measuring end-to-end latency, including preprocessing, inference, and post-processing stages. While earlier YOLO variants primarily relied on convolutional neural network architectures and intermediate explorations such as attention centered designs aimed to improve detection accuracy, YOLOv26 represents a paradigm shift by being designed from the ground up for low power edge devices, emphasizing architectural simplicity and deployment efficiency. To ensure a fair and reproducible evaluation, all models are trained on the same weapon detection dataset under a unified experimental protocol using small scale variants.The experimental results reveal that, despite exhibiting comparable inference times, different models show significantly different real-time performance due to variations in post-processing complexity. Specifically, models such as YOLOv8s, YOLOv9s, and YOLOv11s incur a substantial post-processing overhead, whereas YOLOv10s and YOLOv26s produce compact output representations that drastically reduce post-processing cost.This leads to a clear separation in deployment behavior, where end-to-end latency is reduced from approximately 300 ms to 125–130 ms, effectively doubling the achievable frame rate on embedded hardware. Rather than proposing a universal ranking, the study analyzes the trade offs introduced by architectural evolution and optimization strategies, providing technical criteria to support model selection under resource constrained deployment scenarios and demonstrating that post-processing efficiency, rather than inference speed alone, is the dominant factor in real-time edge performance. Copyright © 2026 Fierro Silva, Del-Valle-Soto, Bran and Varela-Aldás.
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Recent Advances in Multi-Camera Computer Vision for Industry 4.0 and Smart Cities: A Systematic Review
(2026)
Fierro Silva, Carlos Julio
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Carolina Del-Valle-Soto
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Samih M. Mostafa
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Varela Aldas, José
The rapid deployment of surveillance cameras in urban, industrial, and domestic environments has intensified the need for intelligent systems capable of analyzing video streams beyond the limitations of single-camera setups. Unlike traditional single-camera approaches, multi-camera systems expand spatial coverage, reduce blind spots, and enable consistent tracking of people and objects across non-overlapping views, thereby improving robustness against occlusions and viewpoint changes. This article presents a comprehensive review of multi-camera vision systems published between 2020 and 2025, covering application domains including public security and biometrics, intelligent transportation, smart cities and IoT, healthcare monitoring, precision agriculture, industry and robotics, pan–tilt–zoom (PTZ) camera networks, and emerging areas such as retail and forensic analysis. The review synthesizes predominant technical approaches, including deep-learning-based detection, multi-target multi-camera tracking (MTMCT), re-identification (Re-ID), spatiotemporal fusion, and edge computing architectures. Persistent challenges are identified, particularly in inter-camera data association, scalability, computational efficiency, privacy preservation, and dataset availability. Emerging trends such as distributed edge AI, cooperative camera networks, and active perception are discussed to outline future research directions toward scalable, privacy-aware, and intelligent multi-camera infrastructures.
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Real-Time Urban Animal Monitoring Using Transfer Learning-Based Object Detection on Web Platforms
(MDPI AG, 2026-05-13)
Fierro Silva, Carlos Julio
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Sánchez, Carlos A.
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Sánchez, Jorge S.
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Del-Valle-Soto, Carolina
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Velasco, Nancy
This study addresses the growing need for scalable solutions to monitor domestic and stray animals in urban environments. The objective is to develop and evaluate a real-time animal detection system using transfer learning and lightweight object detection models. The methodology includes the adaptation of a custom dataset with annotated images of cats and dogs under real-world conditions, followed by preprocessing, data augmentation, and model fine-tuning. Two architectures, SSD-MobileNet and YOLOv26s, were trained and evaluated using standard metrics such as precision, recall, F1-score, and mAP, as well as operational indicators like inference speed and system responsiveness. The best-performing model was integrated into a web-based platform with real-time detection, mobile access, and automated alerts. Results show that YOLOv26s outperforms SSD-MobileNet, achieving higher precision and recall while significantly reducing false positives and improving background discrimination. The system demonstrates near real-time performance suitable for monitoring applications and effective deployment across different input sources. The discussion findings highlight that integrating detection models with notification and visualization tools enhances practical applicability. Although SSD-MobileNet is suitable for low-resource environments, YOLOv26s provides a better balance between accuracy and reliability, making it more appropriate for real-world intelligent monitoring systems
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Analysis of YOLOv26 Variants (Nano to Extra-Large) for Real-Time Weapon Detection in Video Surveillance
(IEEE, 2026-07)
Fierro Silva, Carlos Julio
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Del-Valle-Soto, Carolina
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Varela Aldas, José
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. © 2026 IEEE.

Fierro Silva, Carlos Julio

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