Forest Fires Detection using Deep Transfer Learning

YANDOUZI, Mimoun; GRARI, Mounir; IDRISSI, Idriss; BOUKABOUS, Mohammed; MOUSSAOUI, Omar; AZIZI, Mostafa; GHOUMID, Kamal; ELMIAD, Aissa KERKOUR

doi:10.14569/ijacsa.2022.0130832

Cited by 21 publications

(19 citation statements)

References 26 publications

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“…Classification is the process of determining a model that explains or classifies a concept or class of data [13]. ML-based fire risk classifiers and predictions are more accurate than traditional methods [14]. In this study, five algorithms were used, namely: Decision Trees (DT), Naive Bayes (NB), Random Forests (RF), Artificial Neural Networks (ANN), and Support Vector Machines (SVM).…”

Section: Classification and Predictive Modelingmentioning

confidence: 99%

Predicting forest fire vulnerability using machine learning approaches in The Mediterranean Region: a case study of Türkiye

Purnama,

Jaya,

Syaufina

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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Forest fires in Türkiye have devastated 2.5 million hectares of habitat over four decades, posing a grave threat to Mediterranean forest ecosystems. This study compares machine learning techniques: Decision Trees (DT), Naive Bayes (NB), Random Forest (RF), Artificial Neural Networks (ANN), and Support Vector Machines (SVM), for predicting forest fire vulnerability. Using a dataset encompassing various factors like precipitation, soil moisture, temperature, humidity, wind speed, land cover, elevation, aspect, slope, proximity to roads/electricity networks, and population density, the models were trained and tested. The dataset classified vulnerability into four classes: very low, low, moderate, and high. Evaluation metrics included overall accuracy, precision, sensitivity, F1-score, Cohen kappa, and cross-validation (CV).RF exhibited the highest performance (accuracy: 0.80, precision: 0.78, sensitivity: 0.80, F1-score: 0.78, Cohen kappa: 0.71, average CV: 0.71), predicting fire vulnerability classes very low (14.99%), low (0.68%), moderate (65.41%), and high (18.90%) with notable accuracy. DT yielded consistent results, while NB performed stably, though slightly lower than RF and DT. However, ANN and SVM demonstrated lower performance and higher variability. These findings advocate for RF as the most accurate algorithm for forest fire risk prediction, emphasizing its crucial role in proactive fire risk management strategies.

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Section: Classification and Predictive Modelingmentioning

confidence: 99%

Predicting forest fire vulnerability using machine learning approaches in The Mediterranean Region: a case study of Türkiye

Purnama,

Jaya,

Syaufina

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…In the past few years, CNNs have shown impressive results in the fields of image processing and object detection. A CNN is a type of neural network specifically designed for image recognition [14], [15]. It is composed of several layers, each of which performs a specific task.…”

Section: Convolutional Neural Network Architecturesmentioning

confidence: 99%

Image and video-based crime prediction using object detection and deep learning

Boukabous¹,

Azizi²

2023

Bulletin EEI

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In recent years, the use of artificial intelligence (AI) for image and video-based crime detection has gained significant attention from law enforcement agencies and security experts. Indeed, deep learning (DL) models can learn complex patterns from data and help law enforcement agencies save time and resources by automatically identifying and tracking potential criminals. This contributes to make deep investigations and better steer their targets’ searches. Among others, handheld firearms and bladed weapons are the most frequent objects encountered at crime scenes. In this paper, we propose a DL-based surveillance system that can detect the presence of tracked objects, such as handheld firearms and bladed weapons, as well as may proceed to alert authorities regarding eventual threats before an incident occurs. After making a comparison of different DL-based object detection techniques, such as you only look once (YOLO), single shot multibox detector (SSD), or faster region-based convolutional neural networks (R-CNN), YOLO achieves the optimal balance of mean average precision (mAP) and inference speed for real-time prediction. Thus, we retain YOLOv5 for the implementation of our solution.

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“…When selecting a backbone model for object detection, we have several options to consider, including VGG-16, VGG-19, and ResNet50, which have been found to be effective in our previous research [4]. It is important to carefully evaluate the strengths and weaknesses of each model in our object detection system to determine which one performs best on our specific dataset.…”

Section: Backbone Model Choicementioning

confidence: 99%

“…The second technique is imagery-based; it uses images coming from fixed cameras, satellites, or drones. It provides authorities with a bird-eye view of the fire and its precise location [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…We remind that the traditional approach uses only visual analysis of images taken by satellites or aerial cameras. The existing system only detects wildfires within the camera view; it cannot identify the exact location of the fire [4]. This gap led us to propose, in this paper, an automated system that helps identify potential forest fires using object detection algorithms, such as YOLO (You Only Look Once) and R-CNN (Region-based Convolutional Neural Net-work).…”

Section: Introductionmentioning

confidence: 99%

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Investigation of Combining Deep Learning Object Recognition with Drones for Forest Fire Detection and Monitoring

YANDOUZI¹,

GRARI²,

BERRAHAL³

et al. 2023

IJACSA

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Forest fires are a global environmental problem that can cause significant damage to natural resources and human lives. The increasing frequency and severity of forest fires have resulted in substantial losses of natural resources. To mitigate this, an effective fire detection and monitoring system is crucial. This work aims to explore and review the current advancement in the field of forest fire detection and monitoring using both drones or unmanned aerial vehicles (UAVs), and deep learning techniques. The utilization of drones fully equipped with specific sensors and cameras provides a cost-effective and efficient solution for real-time monitoring and early fire detection. In this paper, we conduct a comprehensive analysis of the latest developments in deep learning object detection, such as YOLO (You Only Look Once), R-CNN (Region-based Convolutional Neural Network), and their variants, with a focus on their potential application in the field of forest fire monitoring. The performed experiments show promising results in multiple metrics, making it a valuable tool for fire detection and monitoring.

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Forest Fires Detection using Deep Transfer Learning

Cited by 21 publications

References 26 publications

Predicting forest fire vulnerability using machine learning approaches in The Mediterranean Region: a case study of Türkiye

Predicting forest fire vulnerability using machine learning approaches in The Mediterranean Region: a case study of Türkiye

Image and video-based crime prediction using object detection and deep learning

Investigation of Combining Deep Learning Object Recognition with Drones for Forest Fire Detection and Monitoring

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