Impact of Colour Space Transformation on Smoke Detection Accuracy using RESNET50

Iraqi journal of data science., Journal Year: 2025, Volume and Issue: 2(1), P. 27 - 39

Published: Jan. 30, 2025

Detecting smoke that precedes fire is a vital matter since it will detect incidents in very early stage these have high catastrophic effects on people's lives as well industrial matters. In order to produce more reliable detection system, this article, we dove deeper examine the effect of colour conversion captured footage enhance percentage using pre-trained CNN model (ResNet50) was altered do binary classification and trained dataset consists non-smoke scenario images. We examined system footage's original status (RGB) also tested four spaces (HSV, YCbCr, LAB, grayscale). The testing results showed HSV had highest accuracy 92.1% lowest errors during training testing. Regarding accuracy, after RGB, finally, grayscale. Grayscale results, with 85.4%. These indicate affect quality them would improve systems.

Language: Английский

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A Systematic Review of UAV Structure and Monitoring Models for Forest Fire Detection

Highlights in Science Engineering and Technology, Journal Year: 2025, Volume and Issue: 134, P. 81 - 87

Published: March 30, 2025

Forest fires pose a significant threat to ecosystems and economic development. In recent years, unmanned aerial vehicles (UAVs) have emerged as critical technology for forest fire monitoring due their high mobility, low cost, real-time surveillance capabilities. This paper provides systematic review of research progress on UAV-based monitoring, focusing three key aspects: hardware design, detection algorithm improvement, multi-sensor data fusion technologies. First, it summarizes optimization strategies UAV hardware, including sensor configurations, wing power supply improvements, aimed at enhancing flight stability environmental adaptability. Second, analyzes advancements in algorithms, particularly the performance enhancement lightweight modifications deep learning models, explores applicability high-noise environments. Finally, evaluates potential techniques improve accuracy by integrating temperature, smoke, image data. Despite advantages UAVs challenges remain, such limitations, trade-off between processing, complexity coordination. Future should focus optimization, development novel refinement integration further advance applications monitoring.

Language: Английский

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Dynamic fire and smoke detection module with enhanced feature integration and attention mechanisms

Pattern Analysis and Applications, Journal Year: 2025, Volume and Issue: 28(2)

Published: April 5, 2025

Language: Английский

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Improving Fire and Smoke Detection with You Only Look Once 11 and Multi-Scale Convolutional Attention

Fire, Journal Year: 2025, Volume and Issue: 8(5), P. 165 - 165

Published: April 22, 2025

Fires pose significant threats to human safety, health, and property. Traditional methods, with their inefficient use of features, struggle meet the demands fire detection. You Only Look Once (YOLO), as an efficient deep learning object detection framework, can rapidly locate identify smoke objects in visual images. However, research utilizing latest YOLO11 for remains sparse, addressing scale variability well practicality models continues be a focus. This study first compares classic YOLO series analyze its advantages tasks. Then, tackle challenges model practicality, we propose Multi-Scale Convolutional Attention (MSCA) mechanism, integrating it into create YOLO11s-MSCA. Experimental results show that outperforms other by balancing accuracy, speed, practicality. The YOLO11s-MSCA performs exceptionally on D-Fire dataset, improving overall accuracy 2.6% recognition 2.8%. demonstrates stronger ability small objects. Although remain handling occluded targets complex backgrounds, exhibits strong robustness generalization capabilities, maintaining performance complicated environments.

Language: Английский

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Real-Time Fatigue Detection Algorithms Using Machine Learning for Yawning and Eye State

Sensors, Journal Year: 2024, Volume and Issue: 24(23), P. 7810 - 7810

Published: Dec. 6, 2024

Drowsiness while driving is a major factor contributing to traffic accidents, resulting in reduced cognitive performance and increased risk. This article gives complete analysis of real-time, non-intrusive sleepiness detection system based on convolutional neural networks (CNNs). The device analyses video data recorded from an in-vehicle camera monitor drivers' facial expressions detect fatigue indicators such as yawning eye states. built strong architecture was trained using diversified dataset under varying lighting circumstances angles. It uses Haar cascade classifiers for area extraction advanced image processing algorithms diagnosis. results demonstrate that the obtained 96.54% testing accuracy, demonstrating efficiency behavioural frequency state improve performance. findings show CNN-based architectures can address public safety concerns, minimizing accidents caused by drowsy driving. study not only emphasizes need deep learning establishing dependable practical driver monitoring systems, but it also lays groundwork future improvements, incorporation new physiological measurements. suggested solution big step towards increasing road reducing risks associated with weariness.

Language: Английский

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A Smoke and Fire Detection Model Based on Improved YOLO11

Published: Dec. 27, 2024

Language: Английский

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