Fire Detection based on a Two-Dimensional Convolutional Neural Network and Temporal Analysis

Venâncio, Pedro Vinícius Almeida Borges de; Rezende, Tamires Martins; Lisboa, Adriano C.; Barbosa, Adriano Vilela

doi:10.1109/la-cci48322.2021.9769824

Cited by 8 publications

(2 citation statements)

References 9 publications

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“…Similarly, De Venâncio et al proposed a two-dimensional deep CNN that integrated object detection with tracking to analyze temporal behavior and decrease false alarms from objects, such as clouds and car lights. Their approach reduced the 60% false positive rate [33]. Temporal-analysis methods and false-alarm reduction techniques are primarily applied in fire detection systems.…”

Section: Reducing False Alarms During Real-time Monitoringmentioning

confidence: 99%

“…Talaat et al [27] proposed a smart fire detection system that enhances accuracy and reduces false alarms. Similarly, optimized deep learning models and temporal-analysis techniques have been explored to improve detection accuracy and reduce false detection rates [28][29][30][31][32][33]. According to the bibliometric study by Luo et al [34], various developed and developing countries have adopted AI technology for safety research on construction sites.…”

Section: Introductionmentioning

confidence: 99%

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Vision-Based Construction Safety Monitoring Utilizing Temporal Analysis to Reduce False Alarms

Zaidi,

Yang,

Abbas

et al. 2024

Buildings

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Construction safety requires real-time monitoring due to its hazardous nature. Existing vision-based monitoring systems classify each frame to identify safe or unsafe scenes, often triggering false alarms due to object misdetection or false detection, which reduces the overall monitoring system’s performance. To overcome this problem, this research introduces a safety monitoring system that leverages a novel temporal-analysis-based algorithm to reduce false alarms. The proposed system comprises three main modules: object detection, rule compliance, and temporal analysis. The system employs a coordination correlation technique to verify personal protective equipment (PPE), even with partially visible workers, overcoming a common monitoring challenge on job sites. The temporal-analysis module is the key component that evaluates multiple frames within a time window, triggering alarms when the hazard threshold is exceeded, thus reducing false alarms. The experimental results demonstrate 95% accuracy and an F1-score in scene classification, with a notable 2.03% average decrease in false alarms during real-time monitoring across five test videos. This study advances knowledge in safety monitoring by introducing and validating a temporal-analysis-based algorithm. This approach not only improves the reliability of safety-rule-compliance checks but also addresses challenges of misdetection and false alarms, thereby enhancing safety management protocols in hazardous environments.

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Section: Reducing False Alarms During Real-time Monitoringmentioning

confidence: 99%