Smart Drone Surveillance System Based on AI and on IoT Communication in Case of Intrusion and Fire Accident

Hoang, Minh Long

doi:10.3390/drones7120694

Cited by 14 publications

(4 citation statements)

References 39 publications

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“…YOLOv8 uses an anchor-free detection method to predict the target, which improves the detection speed and accuracy [21]. However, some issues remain in handling real-world campus surveillance scenarios, such as crowded crowds, mutual occlusion between members, and computational performance [22]. To solve these problems, this paper proposes a lightweight YOLOv8 model.…”

Section: Overview Of Yolov8mentioning

confidence: 99%

SGST-YOLOv8: An Improved Lightweight YOLOv8 for Real-Time Target Detection for Campus Surveillance

Cheng,

Chao,

Yang

et al. 2024

Applied Sciences

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Real-time target detection plays an important role in campus intelligent surveillance systems. This paper introduces Soft-NMS, GSConv, Triplet Attention, and other advanced technologies to propose a lightweight pedestrian and vehicle detection model named SGST-YOLOv8. In this paper, the improved YOLOv8 model is trained on the self-made dataset, and the tracking algorithm is combined to achieve an accurate and efficient real-time pedestrian and vehicle tracking detection system. The improved model achieved an accuracy of 88.6%, which is 1.2% higher than the baseline model YOLOv8. Additionally, the mAP0.5:0.95 increased by 3.2%. The model parameters and GFLOPS reduced by 5.6% and 7.9%, respectively. In addition, this study also employed the improved YOLOv8 model combined with the bot sort tracking algorithm on the website for actual detection. The results showed that the improved model achieves higher FPS than the baseline YOLOv8 model when detecting the same scenes, with an average increase of 3–5 frames per second. The above results verify the effectiveness of the improved model for real-time target detection in complex environments.

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Section: Overview Of Yolov8mentioning

confidence: 99%

SGST-YOLOv8: An Improved Lightweight YOLOv8 for Real-Time Target Detection for Campus Surveillance

Cheng,

Chao,

Yang

et al. 2024

Applied Sciences

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“…Rotary-wing UAVs, primarily equipped with cameras, are the predominant type for surveillance, although some models utilize laser scanners. An SSDS [4] was successfully developed to detect and track the objects concerned in case of intrusion and fire accidents with the support of AI models and IoT communication. Computer vision models, YOLOv8 and Cascade Classifier, were trained and implemented in the workstation for object classification.…”

Section: Literaure Reviewmentioning

confidence: 99%

Security Surveillance UAV using Analog System

2024

IJSREM

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Unmanned Aerial Vehicles (UAVs) have revolutionized surveillance presented various applications across domains. In this research paper, we explore an unconventional approach by leveraging analog systems for security surveillance UAVs drones. While digital systems dominate the UAV landscape, analog technology offers unique advantages in terms of simplicity, robustness, and real-time responsiveness. Our study investigates the design, implementation, and performance evaluation of an analog-based UAV system tailored for security applications. We delve into the intricacies of analog signal processing, sensor integration, and communication protocols to create a reliable and efficient surveillance platform. By bridging the gap between traditional analog techniques and modern security needs, our proposed system succeeds at enhance situational awareness, minimize latency, and ensure seamless operation in challenging environments for real-time tracking and monitoring. Key Words: Analog System, Drone, GPS Telemetry, Real-Time Surveillance, UAV, Wireless Communication

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“…On the other hand, Machine learning [ML] [ 18 , 19 , 20 ] approaches have demonstrated their high potential effectiveness in healthcare monitoring [ 21 ]. In [ 22 ], a support vector machine (SVM) model was implemented to predict the mental stress condition from the obtained heart rate.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Machine Learning Algorithms for Heartbeat Detection Based on Accelerometric Signals Produced by a Smart Bed

Hoang,

Matrella,

Ciampolini

2024

Sensors

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This work aims to compare the performance of Machine Learning (ML) and Deep Learning (DL) algorithms in detecting users’ heartbeats on a smart bed. Targeting non-intrusive, continuous heart monitoring during sleep time, the smart bed is equipped with a 3D solid-state accelerometer. Acceleration signals are processed through an STM 32-bit microcontroller board and transmitted to a PC for recording. A photoplethysmographic sensor is simultaneously checked for ground truth reference. A dataset has been built, by acquiring measures in a real-world set-up: 10 participants were involved, resulting in 120 min of acceleration traces which were utilized to train and evaluate various Artificial Intelligence (AI) algorithms. The experimental analysis utilizes K-fold cross-validation to ensure robust model testing across different subsets of the dataset. Various ML and DL algorithms are compared, each being trained and tested using the collected data. The Random Forest algorithm exhibited the highest accuracy among all compared models. While it requires longer training time compared to some ML models such as Naïve Bayes, Linear Discrimination Analysis, and K-Nearest Neighbour Classification, it keeps substantially faster than Support Vector Machine and Deep Learning models. The Random Forest model demonstrated robust performance metrics, including recall, precision, F1-scores, macro average, weighted average, and overall accuracy well above 90%. The study highlights the better performance of the Random Forest algorithm for the specific use case, achieving superior accuracy and performance metrics in detecting user heartbeats in comparison to other ML and DL models tested. The drawback of longer training times is not too relevant in the long-term monitoring target scenario, so the Random Forest model stands out as a viable solution for real-time ballistocardiographic heartbeat detection, showcasing potential for healthcare and wellness monitoring applications.

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Smart Drone Surveillance System Based on AI and on IoT Communication in Case of Intrusion and Fire Accident

Cited by 14 publications

References 39 publications

SGST-YOLOv8: An Improved Lightweight YOLOv8 for Real-Time Target Detection for Campus Surveillance

SGST-YOLOv8: An Improved Lightweight YOLOv8 for Real-Time Target Detection for Campus Surveillance

Security Surveillance UAV using Analog System

Comparison of Machine Learning Algorithms for Heartbeat Detection Based on Accelerometric Signals Produced by a Smart Bed

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