Forests play a vital part in our lives by providing numerous resources such as minerals and materials needed for production. Forests purify the air by absorbing carbon dioxide and giving out oxygen [1
], which is fundamental for human survival, and play an important role in providing habitats for several animal species. Forests act as a shield against sandstorms, thus preventing crops and maintaining balance in the ecological system. Forest fire cases have increased in recent years because of climate change [2
]. The high temperature and dry weather result in fire, which destroys environment, wildlife, and natural resources, thus directly affecting human lives. Forests with coniferous (needle or cone) trees are more likely to catch fire than deciduous (leaved) trees due to the large quantity of sap, a highly flammable material, in their branches [4
]. Coniferous trees grow very near to each other, resulting in a faster rate of fire spreading. Due to forest fires, millions of acres of forest lands are destroyed every year, causing significant economic losses. Several countries, such as America, Australia, Canada, and Brazil, have suffered from forest fire destruction.
The Australian fire in 2020 was the most devastating fire, resulting in losses of forest resources, animals, and human lives. It is estimated that around 14 million acres of forest land were burnt, about half a million animals and 23 people died, and 1500 houses were burned [5
]. In 2018 and 2019, California forests and the Amazon rainforest suffered from fires that burnt millions of acres of land, causing huge losses [6
]. In America, 85% of forest fires occurring between 1992 and 2015 were man-made, while 15% occurred naturally, due to factors such as lightning and climate change. This man-made forest fire could have been avoided by restricting human activities. As per [7
], there has been a reduction in forest fire cases since the emergence of the COVID-19 global pandemic, during which several countries imposed total lockdown, reducing human activities [8
]. Moreover, early forest fire detection could also be important in reducing the risk of large-scale forest fires by giving firefighters the opportunity and resources to extinguish the fire in an early stage.
With the aim of protecting forests from fire, governments around the world have stressed the importance of developing strategies for intelligent surveillance and detection of forest fires. The accuracy of detection and alerting the respective authorities is a significant factor that can reduce forest fire risks, which in the case of traditional human monitoring, affects the reliability of the issue. The Internet of Things (IoT), where intelligent devices are connected to the Internet, makes up an integral part of smart cities by applying technologies such as sensors, cloud computing, and wireless network [9
]. These IoT devices [10
] generate a large amount of data, which can be processed and analyzed by applying artificial intelligence (AI). Due to this huge amount of generated data, computer vision has emerged for intelligent surveillance.
Deep learning for detection and recognition is an integral part of computer vision technologies [11
]. Deep neural networks (DNNs) that incorporate deep learning [12
] have made it possible to solve real world problems more efficiently and have become important for intelligent surveillance [13
]. Recently, researchers’ interest in DNNs, such as the convolutional neural network (CNN), which is symmetric, has intensified due to two main factors. Firstly, data storage technology has become cheaper, and secondly, high-performance graphic processing units (GPUs) have met the high computing power requirements. However, the requirement for large datasets is still the key problem for the development of better models in solving real-world tasks.
DNNs can be very useful for early forest fire detection and sending important information to the relevant authorities for necessary actions. Recently, several deep learning-based fire and smoke detection mechanisms have been proposed that achieved good results [15
]. Continuous surveillance of the forest results in the detection of fire that is helpful for relevant departments to take action on time and prevent the fire from becoming a large-scale disaster. In this research, we aim to provide a mechanism for early detection of forest fires that can aid fire departments and disaster relief teams in responding promptly and reducing the fires’ impact on the environment, society, and the economy. The contributions of this research are:
Presenting the literature of computer vision-based forest fire localization and classification methods in forest and wildland environments.
Making use of the newly created dataset, this research further improves the detection accuracy in classifying fire and no-fire images of the forest fire detection dataset that focuses on forest settings unlike previous wildfire research that considers wildlands including bushes and farmlands etc.
Proposing a CNN-based transfer learning approach named FFireNet for forest fire classification on the local dataset. The solution explores the MobileNetV2 model by exploiting the trained weights of the convolutional base and adding fully connected layers for learning complex features and classification.
Evaluating the proposed FFireNet method and comparing it with other CNN models on the forest fire dataset using various performance metrics to validate the performance of the proposed approach.
The remainder of this work is structured as follows. Section 2
details the review of the literature on vision-based forest fire localization and classification methods; Section 3
presents the forest fire detection system for the forest fire classification problem; Section 4
provides details about the newly created dataset and the data augmentation; detailed performance evaluation is presented in Section 5
; and Section 6
concludes this work.
2. Literature Review
In recent years, several studies on forest fire detection systems have been conducted by considering classification and localization, of either only flame or smoke, or both. In [17
], the authors proposed a smoke detection method based on synthetic smoke images using Faster R-CNN. In the proposed method, synthetic images were created by inserting smoke or simulative smoke images into the forest background image. Anim Hossain et al. [18
] proposed a forest fire detection method using color and multi-color space local binary patterns for smoke and flame signatures and artificial neural networks. Their proposed approach detects flame and smoke using the aerial dataset. The authors in [19
] proposed a hybrid solution using long short-term memory (LSTM) and You Only Look Once (YOLO) for the detection of smoke in a wildfire environment. In the proposed solution, the authors used a lightweight teacher–student LSTM that reduced the number of layers with better smoke detection results. Another method [20
] explored the integration of fog computing and CNN to detect fire images. In the subsequent sections, we explore the forest fire localization and classification work previously proposed by the researchers in the literature.
Alexandrov et al. [21
] analyzed different machine learning and CNN models for the localization of forest fires. In their research, the authors used their dataset to analyze the detection accuracy of the considered methods. In [22
], the authors proposed a CNN-based fire detection mechanism. In their proposed method, the full image is first classified using the SVM and transfer learning on AlexNet. After the classification, a fine grain patch classifier is used to locate the fire patch using the pooling-5 layer. The authors found better accuracy of the patch localization than the whole image classification for fire detection.
The authors in [23
] proposed a deep learning-based forest fire detection method for fire localization. In their research, the authors proposed a large-scale YOLOv3 network that locates the fire and smoke in the images. Another study by Li et al. [24
] proposed CNN-based flame detection to monitor forest fire. Their proposed mechanism, named YOLO-Edge, is based on YOLOv4 by replacing the CSPDarkNet53 with MobileNetv3 to detect flame. The MobilNetv3 is used for feature extraction, while the YOLOv4 backbone network is used for flame localization. Table 1
explains the comparative analysis of the localization task for forest fire detection.
This subsection reviews the proposed forest fire detection methods for the classification task in the literature. In [25
], the authors explored different CNN models on their local dataset for a forest fire detection system. Their proposed methodology explored AlexNet, VGG13, Modified VGG13, GoogleNet, and Modified GoogleNet to classify images of Fire and Non-Fire. Kaabi et al. [26
] proposed smoke detection for a forest fire detection system using Deep Believe Network (DBN). In their proposed methodology, DBN is a stacked Restricted Boltzman Machine used for dimensionality reduction, feature extraction, and classification. Zhao et al. in [27
] proposed a deep CNN-based forest fire classification method named Fire_Net. Their proposed Fire_Net model is inspired by an AlexNet 8-layered CNN model with a deeper 15-layered CNN for the classification task.
Chen et al. [28
] proposed a CNN-based forest fire detection mechanism for classification of fire, smoke, and negative images. In their proposed method, CNN models consist of 9 and 17 layers, respectively, named CNN-9 and CNN-17 for classification. In [29
], the authors proposed a novel approach, namely, attention-enhanced bidirectional long short-term memory (Abi-LSTM) to address the forest fire smoke classification problem. In their proposed scheme, Inceptionv3 is used for spatial feature extraction, Bi-LSTM for temporal feature extraction, and the attention network to optimize the classification process. Sousa et al. [30
] proposed a wildfire detection method using transfer learning. In their proposed method, authors exploited the weights of the pre-trained Inceptionv3 model and used it for their new task of fire and not fire classification.
], Govil et al. proposed terrestrial camera-based wildfire detection. Their proposed method used Inceptionv3 model-based classification of the smoke and non-smoke images for wildfire detection. In [32
], the authors proposed deep learning-based forest fire classification. In their proposed approach, the fire and smoke are classified using the ForestResNet method based on the ResNet50 model. Another study [33
] proposed a multilabel classification model for wildfire detection. Their proposed approach used transfer learning based on VGG16, ResNet50, and DenseNet121 for classifying flame, smoke, non-fire, and other objects in the images. Sun et al. [34
] proposed a CNN model for smoke classification in forest fires. Their proposed CNN model applied batch normalization and multi-convolution kernels to optimize and improve classification accuracy. In [35
], authors introduced the novel dataset for forest fire detection named DeepFire. In this research, authors explored the performance of the dataset on different machine learning algorithms and presented a VGG19-based transfer learning solution. Table 2
details the comparative analysis of classification methods for forest fire detection system.
The above-mentioned approaches for forest fire classification show good results in dealing with the classification problem. The forest fire dataset constraint remains the most significant for deep learning and a key obstacle in resolving the forest fire detection problem. In previous research on wildfire detection, the dataset included fire images in urban areas, riots, indoor fire, fires in open fields, industrial fires, among others. However, these datasets do not only represent forest landscapes. As a result, utilizing these datasets for the real-world challenge of forest fire detection may perform sub-optimally. The main aim of a precise classification of the forest fire detection problem is to reduce the number of false alarms and, at the same time, yield higher accuracy. To solve the above-mentioned issues, we present a deep learning-based forest fire detection method for early warning to avoid major disasters. The proposed research would improve forest fire surveillance and detection to make it more efficient and reliable.
This research focuses on a deep learning-based forest fire detection method for early warning. Recently, forest fires have been a serious issue due to natural and man-made climate effects. We presented an artificial intelligence-based forest fire detection method for early detection of forest fires to avoid major disasters. This research discussed vision-based forest fire localization and classification methods in detail. Furthermore, this work made use of the forest fire detection dataset, which solved the classification problem of discriminating Fire and No-Fire images. This research proposed a deep learning method named FFireNet, by leveraging the pre-trained convolutional base of the MobileNetV2 model and adding fully connected layers to solve the new task, which helped to classify forest fires. The performance of the proposed approach for classifying Fire and No-Fire classes was evaluated on different performance metrics and compared with other CNN models. The proposed approach achieved 98.42% accuracy, 1.58% error rate, 99.47% recall, and 97.42% precision in classifying the Fire and No-Fire images. The results of the proposed approach showed superiority over other CNN models such as InceptionV3, Xception, NASNetMobile, and ResNet152V2. The results of the proposed approach were also compared with previous works on forest fire classification task and showed a higher efficiency in terms of considered performance metrics on the newly curated forest fire dataset. The outcomes of the proposed approach are promising for the classification problem considering the new and diverse forest fire detection datasets. Future work will increase the spatial resolution of the images in the forest fire detection dataset. Moreover, a CNN-based image segmentation approach will be proposed to further reduce the rate of false alarms for the forest fire detection problem.