Computer Vision-Based Wildfire Smoke Detection Using UAVs

Rahman, Ehab Ur; Khan, Muhammad Asghar; Algarni, Fahad; Zhang, Yihong; Uddin, M. Irfan; Ullah, Insaf; Ahmad, Hafiz Ishfaq

doi:10.1155/2021/9977939

Cited by 8 publications

(7 citation statements)

References 24 publications

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“…While Unmanned Aerial Vehicle (UAV) patrols have become the primary method for forest-fire prevention, existing detection technologies often struggle to cope with the complexity of forest-fire images captured from high altitudes [3][4][5]. For example, Rahman et al [42] utilized the SSD model, leveraging texture and color information, to equip UAVs with high-speed and high-accuracy capabilities. Challenges include remote locations, small fire spots, light-colored smoke targets, and complex background environments.…”

Section: Discussionmentioning

confidence: 99%

FSNet: Enhancing Forest-Fire and Smoke Detection with an Advanced UAV-Based Network

Wu,

Qian,

et al. 2024

Forests

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Forest fires represent a significant menace to both the ecological equilibrium of forests and the safety of human life and property. Upon ignition, fires frequently generate billowing smoke. The prompt identification and management of fire sources and smoke can efficiently avert the occurrence of extensive forest fires, thereby safeguarding both forest resources and human well-being. Although drone patrols have emerged as a primary method for forest-fire prevention, the unique characteristics of forest-fire images captured from high altitudes present challenges. These include remote distances, small fire points, smoke targets with light hues, and complex, ever-changing background environments. Consequently, traditional target-detection networks frequently exhibit diminished accuracy when handling such images. In this study, we introduce a cutting-edge drone-based network designed for the detection of forest fires and smoke, named FSNet. To begin, FSNet employs the YOCO data-augmentation method to enhance image processing, thereby augmenting both local and overall diversity within forest-fire images. Next, building upon the transformer framework, we introduce the EBblock attention module. Within this module, we introduce the notion of “groups”, maximizing the utilization of the interplay between patch tokens and groups to compute the attention map. This approach facilitates the extraction of correlations among patch tokens, between patch tokens and groups, and among groups. This approach enables the comprehensive feature extraction of fire points and smoke within the image, minimizing background interference. Across the four stages of the EBblock, we leverage a feature pyramid to integrate the outputs from each stage, thereby mitigating the loss of small target features. Simultaneously, we introduce a tailored loss function, denoted as Lforest, specifically designed for FSNet. This ensures the model’s ability to learn effectively and produce high-quality prediction boxes. We assess the performance of the FSNet model across three publicly available forest-fire datasets, utilizing mAP, Recall, and FPS as evaluation metrics. The outcomes reveal that FSNet achieves remarkable results: on the Flame, Corsican, and D-Fire datasets, it attains mAP scores of 97.2%, 87.5%, and 94.3%, respectively, with Recall rates of 93.9%, 87.3%, and 90.8%, respectively, and FPS values of 91.2, 90.7, and 92.6, respectively. Furthermore, extensive comparative and ablation experiments validate the superior performance of the FSNet model.

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Section: Discussionmentioning

confidence: 99%

FSNet: Enhancing Forest-Fire and Smoke Detection with an Advanced UAV-Based Network

Wu,

Qian,

et al. 2024

Forests

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“…is comparison demonstrates the correctness of our continued exploration of multifrequency component fusion to improve GAP. (2) e recognition accuracy of some models exceeds that of models using the FcaNet fusion method. is result verifies the effectiveness of our method of setting different fusion scales in feature maps of different scales.…”

Section: Dct-gap Designmentioning

confidence: 99%

“…erefore, early warning of a re is highly critical. As a clue to determine re occurrence, smoke is produced early and widely distributed and has stable characteristics, which make it a more reliable indicator than ame [2,3]. Traditional smoke detectors are primarily based on particle collection.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Smoke Recognition Based on Deep Convolution and Self-Attention

Zhao

Wang

Jung

et al. 2022

Mathematical Problems in Engineering

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Deep convolutional networks have better smoke recognition performance. However, a lightweight network model and high recognition accuracy cannot be balanced when deployed on hardware with limited computing resources such as edge computing. Based on this background, we propose a novel smoke recognition network that combines convolutional networks (CNN) and self-attention. The core ideas of this framework are as follows: (1) Combine the depthwise convolution and asymmetric convolution of large convolution kernels to construct a lightweight CNN model, and realize multiscale extraction of feature information with slight model complexity. (2) Combined with the self-attention in transformer, a skip-connection branch is designed, which improves the feature extraction capability of the backbone network through parallel processing and fusion of feature map information. (3) Fusion multicomponent discrete cosine transform (DCT) is used to compress channel information and expand the ability of global average pooling (GAP) to aggregate feature maps. The proposed DCT-GAP improves the accuracy of the network without adding additional computational costs. Experimental results show that the proposed CSANet achieves an average accuracy of over 98.3% with 238 M FLOPs and 5.8 M parameters on the homemade smoke dataset, outperforming state-of-the-art competitors.

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“…This makes UAS platforms an excellent option for detailed, small spatial extent observations and active-fire monitoring. In this study, we address the increasing interest in utilizing UAS vehicles in conjunction with computer vision, an advanced branch of artificial intelligence, to improve and automate the detection of fire, smoke, and other fire related phenomena (Rahman et al, 2021;Zhan et al, 2021;Chen et al, 2022). Specifically, our aim is to further these developments by employing the latest "You Only Look Once" (YOLO) detection algorithm to identify specific fire behavior descriptions as defined by the National Wildland Coordinating Group (NWCG).…”

Section: Introductionmentioning

confidence: 99%

Using computer vision to detect and segment fire behavior classifications in UAS-captured images

Lawrence,

de Lemmus

2024

Preprint

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The widely adaptable capabilities of artificial intelligence, in particular deep learning and computer vision has led to significant research output regarding fire and smoke detection. Previous studies often focus on themes like early fire detection, increased operational awareness, and post-fire assessment. To further test the capabilities of deep learning detection in these scenarios, we collected and labeled a unique aerial image dataset that determined whether specific types of fire behavior could be reliably detected in prescribed fire settings. Our 960 labeled images were sourced from over 20.97 hours of UAS video collected during prescribed fire operations covering a large region of Texas and Louisiana, U.S.. National Wildfire Coordinating Group (NWCG) fire behavior observations and descriptions served as a reference for determining fire behavior classes during labeling. YOLOv8 models were trained on NWCG Rank 1-3 fire behavior descriptions in grassland, shrubland, forested, and combined fire regimes within our study area. Models were first trained and validated on isolated image objects of fire behavior, and then on segmenting fire behavior in their original parent images. Models trained using isolated image objects of fire behavior consistently performed at a mAP of 0.808 or higher, with combined fire regimes producing the best results (mAP = 0.897). Most segmentation models performed relatively poorly, except for the forest regime model at a box and mask mAP of 0.59 and 0.611, respectively. Our results indicate that classifying fire behavior with computer vision is possible in most fire regimes and fuel models, whereas segmenting fire behavior around background information is relatively difficult. However, it may be a manageable task with enough data, and when models are developed for a specific fire regime. With an increasing number of destructive wildfires and new challenges confronting fire managers, identifying how new technologies can quickly assess wildfire situations can assist wildfire responder awareness. Our conclusion is that levels of abstraction deeper than mere detection of smoke or fire are possible using computer vision, and could make even more detailed fire monitoring possible.

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Computer Vision-Based Wildfire Smoke Detection Using UAVs

Cited by 8 publications

References 24 publications

FSNet: Enhancing Forest-Fire and Smoke Detection with an Advanced UAV-Based Network

FSNet: Enhancing Forest-Fire and Smoke Detection with an Advanced UAV-Based Network

Lightweight Smoke Recognition Based on Deep Convolution and Self-Attention

Using computer vision to detect and segment fire behavior classifications in UAS-captured images

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