Introduction
Forest fires pose a significant threat to biodiversity, ecosystems, and human communities worldwide, leading to devastating environmental and socio-economic consequences. Rapid detection and timely response are critical for mitigating the impact of forest fires and preventing widespread destruction. Traditional methods of forest fire detection, such as human surveillance and ground- based monitoring systems, have limitations in terms of coverage, scalability, and response time. In recent years, advances in remote sensing technology and deep learning techniques have provided new opportunities for improving the efficiency and effectiveness of forest fire detection systems. The integration of deep convolutional neural networks (CNNs) with remote sensing data offers a promis- ing approach to forest fire detection, leveraging the power of machine learning to analyze large-scale aerial imagery and satellite data. CNNs are particularly well-suited for image classification tasks, capable of automatically learning hierarchical features from raw pixel data and making accurate predictions. By training CNN models on labeled datasets of forest fire imagery, it becomes possible to detect subtle patterns and anomalies indicative of fire outbreaks, enabling early warning systems and proactive firefighting efforts. The development of an efficient model for forest fire detection using deep CNNs addresses the need for robust and scalable solutions capable of operating in real- time and resource-constrained environments. Such a model can be deployed on various platforms, including unmanned aerial vehicles (UAVs), surveillance cameras, and satellite systems, providing continuous monitoring and surveillance of forested areas. By analyzing aerial imagery and satellite data, the model can detect the presence of smoke plumes, thermal anomalies, and other visual cues associated with forest fires, enabling rapid response and intervention by firefighting agencies.
One of the key challenges in forest fire detection using CNNs is the optimization of model performance and computational efficiency, particularly for deployment on edge devices with limited processing power and bandwidth. To address this challenge, researchers have developed lightweight CNN architectures and optimization techniques tailored for real-time inference and low-latency applications. By reducing model complexity and memory footprint while preserving accuracy, these optimized CNN models can deliver efficient and reliable forest fire detection capabilities in resource-constrained environments. In this paper, we propose FireGuard, an efficient model for forest fire detection using deep convolutional neural networks. FireGuard combines state-of-the-art CNN architectures with remote sensing data processing techniques to enable accurate and timely detection of forest fires. We evaluate the performance of FireGuard using real-world aerial imagery and satellite data, demonstrating its effectiveness in detecting forest fires with high precision and recall. By providing early warnings of potential fire incidents, FireGuard aims to enhance forest
fire prevention, response, and management efforts, ultimately contributing to the preservation of natural habitats and the safety of communities residing in forested areas.
Related Works
Image classification is just one of several computer vision problems where convolutional neural networks (CNN) have demonstrated state-of-the-art performance [1]. Integrating them into fire detection systems will make detection much more accurate, which will ultimately lessen the impact that fires have on both the environment and the people who reside there [2]. Due to their high inference memory and processing needs, CNN-based fire detection systems provide the biggest threat when applied to real-world surveillance networks [3]. In this paper, we provide a convolutional neural network (CNN) design that minimizes power consumption without sacrificing computational efficiency. Fire detection, localization, and semantic understanding of the fire scenario are the planned uses of this design, which is based on the SqueezeNet architecture [4]. This model uses smaller convolutional kernels and avoids thick, fully linked layers, which reduces its processing requirements. Our proposed method achieves accuracies that are equivalent to other more advanced models, according to the experimental data [5]. This is mainly because, despite using a moderate amount of CPU resources, it offers improved depth [6]. By considering the many kinds of fire data and the details of the situation at hand, the research also shows that there is a trade-off between efficiency and accuracy in fire detection [7]. Everywhere on Earth, from the densest rainforests to the largest cities, fire accidents pose a serious threat. Although fire detection systems could potentially avoid such incidents, they are extremely costly, produce a high volume of false alarms, necessitate a separate infrastructure, and are not very reliable compared to current hardware and software-based detection systems [8]. Our goal in doing this work is to advance the use of deep learning for fire detection in videos. Computer vision is only one of several fields that has benefited greatly from deep learning’s early successes in using artificial neural networks. The field of deep learning is relatively new [9]. The goal of this project is to offer alternatives to the current systems that address the issues they produce [10].
A system that can save countless lives and resources, is accurate and precise, can detect fires rapidly, and can operate in various conditions [11]. Identifying forest fires can be difficult due to the vast array of forms, textures, and colors that they can assume [12]. The traditional approach to image processing relies heavily on human-generated characteristics, which might not work in every forest environment. The use of adaptive deep learning to study and extract features of forest fires provides the solution to this issue [13]. However, complex activities are beyond the capabilities of individual learners due to their restricted learning and perceptual powers. Another potential source of false positives is students’ tendency to focus on regional expertise rather than global data
[14]. For the aim of forest fire detection in various contexts, this study proposes a novel ensemble learning approach. The fire detection operation is carried out by combining Yolov5 and EfficientDet, two independent learners [15]. You can think of this as the starting point. Second, EfficientNet, a separate individual learner, is responsible for acquiring knowledge about the global environment with the aim of avoiding false positives. At the end of the day, three separate learners’ decisions decide the detection findings [16]. Our trials on this dataset demonstrated that the suggested approach reduced false positives by 51.3% and improved detection performance by 2.5% to 10.9%. Using ensemble learning as its foundation, the study aims to develop a system for forest fire detection. The detection of forest fires is a highly significant topic of research [17]. Forest fire detection using wireless sensor networks is detailed in [18], which also covers the system’s design, implementation, and performance evaluation. The article continues by outlining the numerous forest fire detection methods that employ wireless sensor networks, before delving into their benefits, drawbacks, and potential future developments [19]. Afterwards, [20] suggests a method for detecting forest fires by using UAVs fitted with infrared sensors to keep an eye out for potential danger. This review looks at different approaches to forest fire detection that employ geographic information systems (GIS) and remote sensing, and it talks about how useful and applicable these methods are. In order to better detect forest fires, a deep learning framework called DeepForest is introduced in existing. Photos captured by unmanned aerial vehicles (UAVs) are utilized by this framework.
Methodology
It is remarkable that SqueezeNet is able to achieve a high level of accuracy with a significantly lower number of parameters when compared to more traditional deep neural networks. In 2016, researchers from DeepScale presented it to the public for the first time. Since then, it has garnered a reputation for having small model sizes and minimal processing requirements. Through the utilization of 1x1 convolutional layers, often known as ”squeeze” layers, the major objective of SqueezeNet is to reduce the number of parameters that are used by the network. All of these layers work together to lower the dimensionality of the input feature maps and compress the data in a way that is both computationally efficient and effective. The proposed system architecture is shown in Figure 1.
In order to get the data ready for analysis, there are a few processes that need to be taken. These steps include data purification, filling in missing values, and preparing the data for acceptance by the deep learning model. For the Design of a Schematic: All of the components of the neural network, including the optimization algorithm, activation functions, loss functions, and the total number of layers, have been developed at this point. Conventional wisdom suggests that the design should be informed by the characteristics of the data as well as the nature of the work that is
Figure 1. Proposed System Architecture
currently being done. After the data has been fed into the neural network, the parameters of the model are fine-tuned through the process of backpropagation in order to reduce the loss function as much as possible. Each iteration of the training procedure is carried out until the model reaches an accuracy level that is deemed suitable. Following the completion of the training phase, the model is evaluated on a distinct dataset to determine how well it can generalize and whether or not it has overfit the data at hand. The validation set allows you to make adjustments to the model’s hyperparameters, which will result in the model operating more smoothly. The purpose of this section is to evaluate the performance of the model in real-world scenarios using a new set of data in order to achieve the objective of enhancing the accuracy of the model. Following the completion of the validation and testing processes, it is feasible to bring the model into production so that it can be utilized in real-world scenarios. There are many instances in which this process involves the construction of an interface that allows individuals to engage with the model and the integration of the model with other software systems.
Results and Discussion
It is feasible to build a model that can correctly classify examples into distinct classes when faced with a classification difficulty by utilizing many approaches. Presented in the following table are the performance metrics are given in Table 1 for four separate algorithms used to complete classification tasks. Sorting the table by row should be our first order of business. The proposed SqueezeNet is a FireGuard CNN network and is compared with KNN and SVM in Table 1.
Another widely recognized method for solving classification problems is K-Nearest Neighbors, or KNN for short. A precision score of 78.2805% was achieved by KNN, as shown in the table. This
Table 1. Performance Metrics
METHOD/METRIC | PRECISION | RECALL | ACCURACY |
SVM | 87.2534 | 86.7995 | 87.0627 |
KNN | 79.2901 | 92.0127 | 83.8848 |
SqueezeNet | 98.4476 | 98.4837 | 99.9574 |
means that 78.15 percent of the positive cases predicted by the model were true positives. With a recall score of 91.0526%, KNN proved to be nearly perfect at identifying real positive events. The accuracy score for KNN was 82.8947%, indicating that the model was able to correctly identify the data 82.8947% of the time. You can see a comparison of the KNN, Support Vector Machine (SVM), and SqueezeNet models in the graph below as shown in Figure 2, comparing precision, accuracy, and recall.
Figure 2. Comparison of different models
To effectively complete picture categorization jobs, SqueezeNet is the deep learning algorithm to use. With an impressive recall score of 99.4736% and a precision score of 98.4375%, SqueezeNet stands out among the other four algorithms in the table. So, not only did the model accurately identify 98.4375% of the positive instances it predicted, but it also accurately identified 99.4736% of the actual positive events. This precision best of the four methods was SqueezeNet, which scored 98.9473%. This means that 98.9473% of the samples were properly classified by the model.