Forest-Fire Response System Using Deep-Learning-Based Approaches With CCTV Images and Weather Data

Tran, Dai Quoc; Park, Minsoo; Jeon, Yuntae; Bak, JinYeong; Park, Seunghee

doi:10.1109/access.2022.3184707

Cited by 34 publications

(8 citation statements)

References 47 publications

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“…A study used a deep CNN for recognition and achieved improved accuracy compared with that of traditional methods; however, identifying the entire video input required considerable time in practical applications [36]. A study used an SVM classifier to identify flames; however, different features need to be selected as inputs to the classifier, and the selected features are subjective and cannot be guaranteed to be the best features [37].…”

Section: Image Flame Features and Model Selectionmentioning

confidence: 99%

A Forest Fire Recognition Method Based on Modified Deep CNN Model

Zheng,

Zou,

Gao

et al. 2024

Forests

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Controlling and extinguishing spreading forest fires is a challenging task that often leads to irreversible losses. Moreover, large-scale forest fires generate smoke and dust, causing environmental pollution and posing potential threats to human life. In this study, we introduce a modified deep convolutional neural network model (MDCNN) designed for the recognition and localization of fire in video imagery, employing a deep learning-based recognition approach. We apply transfer learning to refine the model and adapt it for the specific task of fire image recognition. To combat the issue of imprecise detection of flame characteristics, which are prone to misidentification, we integrate a deep CNN with an original feature fusion algorithm. We compile a diverse set of fire and non-fire scenarios to construct a training dataset of flame images, which is then employed to calibrate the model for enhanced flame detection accuracy. The proposed MDCNN model demonstrates a low false alarm rate of 0.563%, a false positive rate of 12.7%, a false negative rate of 5.3%, and a recall rate of 95.4%, and achieves an overall accuracy of 95.8%. The experimental results demonstrate that this method significantly improves the accuracy of flame recognition. The achieved recognition results indicate the model’s strong generalization ability.

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Section: Image Flame Features and Model Selectionmentioning

confidence: 99%

A Forest Fire Recognition Method Based on Modified Deep CNN Model

Zheng,

Zou,

Gao

et al. 2024

Forests

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“…Recent advancements in technology are poised to significantly transform the paradigm of worker safety through the automation of risk assessments. Noteworthy progress has been achieved in the application of computer vision to identify potential hazards (Tran, et al, 2022). However, some studies exhibit limitations in their scope, especially regarding the precise localization of dangers and the evaluation of workers' proximity to such hazards.…”

Section: Ffh-related Safety Monitoringmentioning

confidence: 99%

Deep Learning-Based Pose Estimation for Identifying Potential Fall Hazards of Construction Worker

Park,

Lee,

Choi

et al. 2023

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

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Fall from height (FFH) is one of the major causes of injury and fatalities in construction industry. Deep learning-based computer vision for safety monitoring has gained attention due to its relatively lower initial cost compared to traditional sensing technologies. However, a single detection model that has been used in many related studies cannot consider various contexts at the construction site. In this paper, we propose a deep learning-based pose estimation approach for identifying potential fall hazards of construction workers. This approach can relatively increase the accuracy of estimating the distance between the worker and the fall hazard area compared to the existing methods from the experimental results. Our proposed approach can improve the robustness of worker location estimation compared to existing methods in complex construction site environments with obstacles that can obstruct the worker's position. Also, it is possible to provide information on whether a worker is aware of a potential fall risk area. Our approach can contribute to preventing FFH by providing access information to fall risk areas such as construction site openings and inducing workers to recognize the risk area even in Inattentional blindness (IB) situations

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“…In construction sites, object detection can identify critical elements such as workers, machinery, tools, and other potential hazards, thereby playing a vital role in safety monitoring. However, traditional object detection models typically operate independently, failing to incorporate the broader context of a scene (Jeon et al, 2023;Tran et al, 2022). These models often struggle with complex environments like construction sites, where multiple objects interact dynamically, and understanding these interactions is crucial for effective hazard detection.…”

Section: 1object Detectionmentioning

confidence: 99%

Identifying Hazards in Construction Sites Using Deep Learning-Based Multimodal with CCTV Data

Quoc Tran,

Jeon,

Son

et al. 2023

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

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The use of closed-circuit television (CCTV) for safety monitoring is crucial for reducing accidents in construction sites. However, the majority of currently proposed approaches utilize single detection models without considering the context of CCTV video inputs. In this study, a multimodal detection, and depth map estimation algorithm utilizing deep learning is proposed. In addition, the point cloud of the test site is acquired using a terrestrial laser scanning scanner, and the detected object's coordinates are projected into global coordinates using a homography matrix. Consequently, the effectiveness of the proposed monitoring system is enhanced by the visualization of the entire monitored scene. In addition, to validate our proposed method, a synthetic dataset of construction site accidents is simulated with Twinmotion. These scenarios are then evaluated with the proposed method to determine its precision and speed of inference. Lastly, the actual construction site, equipped with multiple CCTV cameras, is utilized for system deployment and visualization. As a result, the proposed method demonstrated its robustness in detecting potential hazards on a construction site, as well as its real-time detection speed

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Forest-Fire Response System Using Deep-Learning-Based Approaches With CCTV Images and Weather Data

Cited by 34 publications

References 47 publications

A Forest Fire Recognition Method Based on Modified Deep CNN Model

A Forest Fire Recognition Method Based on Modified Deep CNN Model

Deep Learning-Based Pose Estimation for Identifying Potential Fall Hazards of Construction Worker

Identifying Hazards in Construction Sites Using Deep Learning-Based Multimodal with CCTV Data

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