Light-Weight Student LSTM for Real-Time Wildfire Smoke Detection

Forest Fire Smoke Detection Based on Deep Learning Approaches and Unmanned Aerial Vehicle Images

Wildfire poses a significant threat and is considered a severe natural disaster, which endangers forest resources, wildlife, and human livelihoods. In recent times, there has been an increase in the number of wildfire incidents, and both human involvement with nature and the impacts of global warming play major roles in this. The rapid identification of fire starting from early smoke can be crucial in combating this issue, as it allows firefighters to respond quickly to the fire and prevent it from spreading. As a result, we proposed a refined version of the YOLOv7 model for detecting smoke from forest fires. To begin, we compiled a collection of 6500 UAV pictures of smoke from forest fires. To further enhance YOLOv7's feature extraction capabilities, we incorporated the CBAM attention mechanism. Then, we added an SPPF+ layer to the network's backbone to better concentrate smaller wildfire smoke regions. Finally, decoupled heads were introduced into the YOLOv7 model to extract useful information from an array of data. A BiFPN was used to accelerate multi-scale feature fusion and acquire more specific features. Learning weights were introduced in the BiFPN so that the network can prioritize the most significantly affecting characteristic mapping of the result characteristics. The testing findings on our forest fire smoke dataset revealed that the proposed approach successfully detected forest fire smoke with an AP50 of 86.4%, 3.9% higher than previous single- and multiple-stage object detectors.

A Wildfire Smoke Detection System Using Unmanned Aerial Vehicle Images Based on the Optimized YOLOv5

Wildfire is one of the most significant dangers and the most serious natural catastrophe, endangering forest resources, animal life, and the human economy. Recent years have witnessed a rise in wildfire incidents. The two main factors are persistent human interference with the natural environment and global warming. Early detection of fire ignition from initial smoke can help firefighters react to such blazes before they become difficult to handle. Previous deep-learning approaches for wildfire smoke detection have been hampered by small or untrustworthy datasets, making it challenging to extrapolate the performances to real-world scenarios. In this study, we propose an early wildfire smoke detection system using unmanned aerial vehicle (UAV) images based on an improved YOLOv5. First, we curated a 6000-wildfire image dataset using existing UAV images. Second, we optimized the anchor box clustering using the K-mean++ technique to reduce classification errors. Then, we improved the network's backbone using a spatial pyramid pooling fast-plus layer to concentrate small-sized wildfire smoke regions. Third, a bidirectional feature pyramid network was applied to obtain a more accessible and faster multi-scale feature fusion. Finally, network pruning and transfer learning approaches were implemented to refine the network architecture and detection speed, and correctly identify small-scale wildfire smoke areas. The experimental results proved that the proposed method achieved an average precision of 73.6% and outperformed other one- and two-stage object detectors on a custom image dataset.

FFireNet: Deep Learning Based Forest Fire Classification and Detection in Smart Cities

Forests are a vital natural resource that directly influences the ecosystem. Recently, forest fire has been a serious issue due to natural and man-made climate effects. For early forest fire detection, an artificial intelligence-based forest fire detection method in smart city application is presented to avoid major disasters. This research presents a review of the vision-based forest fire localization and classification methods. Furthermore, this work makes use of the forest fire detection dataset, which solves the classification problem of discriminating fire and no-fire images. This work proposes 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, that is, the forest fire recognition problem, which helps in classifying images as forest fires based on extracted features which are symmetrical. The performance of the proposed solution for classifying fire and no-fire was evaluated using different performance metrics and compared with other CNN models. The results show that the proposed approach achieves 98.42% accuracy, 1.58% error rate, 99.47% recall, and 97.42% precision in classifying the fire and no-fire images. The outcomes of the proposed approach are promising for the forest fire classification problem considering the unique forest fire detection dataset.

Adversarial Fusion Network for Forest Fire Smoke Detection

Recent advances suggest that deep learning has been widely used to detect smoke for early forest fire warnings. Despite its remarkable success, this approach has a number of problems in real life application. Deep neural networks only learn deep and abstract representations, while ignoring shallow and detailed representations. In addition, previous models have been trained on source domains but have generalized weakly on unseen domains. To cope with these problems, in this paper, we propose an adversarial fusion network (AFN), including a feature fusion network and an adversarial feature-adaptation network for forest fire smoke detection. Specifically, the feature fusion network is able to learn more discriminative representations by fusing abstract and detailed features. Meanwhile, the adversarial feature adaptation network is employed to improve the generalization ability and transfer gains of the AFN. Comprehensive experiments on two self-built forest fire smoke datasets, and three publicly available smoke datasets, validate that our method significantly improves the performance and generalization of smoke detection, particularly the accuracy of the detection of small amounts of smoke.

FIgLib & SmokeyNet: Dataset and Deep Learning Model for Real-Time Wildland Fire Smoke Detection

The size and frequency of wildland fires in the western United States have dramatically increased in recent years. On high-fire-risk days, a small fire ignition can rapidly grow and become out of control. Early detection of fire ignitions from initial smoke can assist the response to such fires before they become difficult to manage. Past deep learning approaches for wildfire smoke detection have suffered from small or unreliable datasets that make it difficult to extrapolate performance to real-world scenarios. In this work, we present the Fire Ignition Library (FIgLib), a publicly available dataset of nearly 25,000 labeled wildfire smoke images as seen from fixed-view cameras deployed in Southern California. We also introduce SmokeyNet, a novel deep learning architecture using spatiotemporal information from camera imagery for real-time wildfire smoke detection. When trained on the FIgLib dataset, SmokeyNet outperforms comparable baselines and rivals human performance. We hope that the availability of the FIgLib dataset and the SmokeyNet architecture will inspire further research into deep learning methods for wildfire smoke detection, leading to automated notification systems that reduce the time to wildfire response.