An Improved Forest Fire Detection Method Based on the Detectron2 Model and a Deep Learning Approach

Abdusalomov, Akmalbek Bobomirzaevich; Islam, Bappy MD Siful; Nasimov, Rashid; Mukhiddinov, Mukhriddin; Whangbo, Taeg Keun

doi:10.3390/s23031512

Cited by 81 publications

(35 citation statements)

References 64 publications

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“…Refs. [ 26 , 27 ] deep learning approach (Detectron2) and a new special convolutional neural network (Improved YOLOv3) to build detection platforms or systems for high accuracy and fast detection and recognition of forest fire image recognition (both day and night), respectively. The scope of this paper refers to fire risk assessment and prediction, and does not include image recognition and detection techniques.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Models Using SHapley Additive exPlanation for Fire Risk Assessment Mode and Effects Analysis of Stadiums

Fan

Zhang

et al. 2023

Sensors

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Machine learning methods can establish complex nonlinear relationships between input and response variables for stadium fire risk assessment. However, the output of machine learning models is considered very difficult due to their complex “black box” structure, which hinders their application in stadium fire risk assessment. The SHapley Additive exPlanations (SHAP) method makes a local approximation to the predictions of any regression or classification model so as to be faithful and interpretable, and assigns significant values (SHAP value) to each input variable for a given prediction. In this study, we designed an indicator attribute threshold interval to classify and quantify different fire risk category data, and then used a random forest model combined with SHAP strategy in order to establish a stadium fire risk assessment model. The main objective is to analyze the impact analysis of each risk characteristic on four different risk assessment models, so as to find the complex nonlinear relationship between risk characteristics and stadium fire risk. This helps managers to be able to make appropriate fire safety management and smart decisions before an incident occurs and in a targeted manner to reduce the incidence of fires. The experimental results show that the established interpretable random forest model provides 83% accuracy, 86% precision, and 85% recall for the stadium fire risk test dataset. The study also shows that the low level of data makes it difficult to identify the range of decision boundaries for Critical mode and Hazardous mode.

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Section: Resultsmentioning

confidence: 99%

Machine Learning Models Using SHapley Additive exPlanation for Fire Risk Assessment Mode and Effects Analysis of Stadiums

Fan

Zhang

et al. 2023

Sensors

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“…However, in ultrasound images, speckle noise is significant. In medical images, such as echocardiogram images, speckle noise may convey crucial information about pathologies in tissues or organs [46]. For example, methods have been created to identify fibrosis by analyzing speckle noises present in echocardiogram images [47].…”

Section: Experiments and Resultsmentioning

confidence: 99%

Synthetic Echocardiograms Generation Using Diffusion Models

Pellicer,

Singh Yadav,

Bhagtani

et al. 2023

Preprint

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An echocardiogram is a video sequence of a human heart captured using ultrasound imaging. It shows heart structure and motion and helps in diagnosis of cardiovascular diseases. Deep learning methods, which require large amounts of training data have shown success in using echocardiograms to detect cardiovascular disorders such as valvular heart disease. Large datasets of echocardiograms that can be used for machine learning training are scarce. One way to address this problem is to use modern machine learning generative methods to generate synthetic echocardiograms that can be used for machine learning training. In this paper, we propose a video diffusion method for the generation of echocardiograms. Our method uses a 3D selfattention mechanism and a super-resolution model. We demonstrate that our proposed method generates echocardiograms with higher resolution and with lesser artifacts, compared to existing echocardiogram generation methods.

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“…The results did not drastically shift when we rotated the fire images by over 15 degrees. Contrarily, we might lose the fire image’s region of interest (ROI) when we rotate them by over 15 degrees [ 36 ].…”

Section: Proposed Workmentioning

confidence: 99%

A YOLOv6-Based Improved Fire Detection Approach for Smart City Environments

Saydirasulovich

Abdusalomov

Jamil

et al. 2023

Sensors

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Authorities and policymakers in Korea have recently prioritized improving fire prevention and emergency response. Governments seek to enhance community safety for residents by constructing automated fire detection and identification systems. This study examined the efficacy of YOLOv6, a system for object identification running on an NVIDIA GPU platform, to identify fire-related items. Using metrics such as object identification speed, accuracy research, and time-sensitive real-world applications, we analyzed the influence of YOLOv6 on fire detection and identification efforts in Korea. We conducted trials using a fire dataset comprising 4000 photos collected through Google, YouTube, and other resources to evaluate the viability of YOLOv6 in fire recognition and detection tasks. According to the findings, YOLOv6’s object identification performance was 0.98, with a typical recall of 0.96 and a precision of 0.83. The system achieved an MAE of 0.302%. These findings suggest that YOLOv6 is an effective technique for detecting and identifying fire-related items in photos in Korea. Multi-class object recognition using random forests, k-nearest neighbors, support vector, logistic regression, naive Bayes, and XGBoost was performed on the SFSC data to evaluate the system’s capacity to identify fire-related objects. The results demonstrate that for fire-related objects, XGBoost achieved the highest object identification accuracy, with values of 0.717 and 0.767. This was followed by random forest, with values of 0.468 and 0.510. Finally, we tested YOLOv6 in a simulated fire evacuation scenario to gauge its practicality in emergencies. The results show that YOLOv6 can accurately identify fire-related items in real time within a response time of 0.66 s. Therefore, YOLOv6 is a viable option for fire detection and recognition in Korea. The XGBoost classifier provides the highest accuracy when attempting to identify objects, achieving remarkable results. Furthermore, the system accurately identifies fire-related objects while they are being detected in real-time. This makes YOLOv6 an effective tool to use in fire detection and identification initiatives.

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An Improved Forest Fire Detection Method Based on the Detectron2 Model and a Deep Learning Approach

Cited by 81 publications

References 64 publications

Machine Learning Models Using SHapley Additive exPlanation for Fire Risk Assessment Mode and Effects Analysis of Stadiums

Machine Learning Models Using SHapley Additive exPlanation for Fire Risk Assessment Mode and Effects Analysis of Stadiums

Synthetic Echocardiograms Generation Using Diffusion Models

A YOLOv6-Based Improved Fire Detection Approach for Smart City Environments

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