Application of Artificial Intelligence in UAV platforms for Early Forest Fire Detection

Kinaneva, Diyana; Hristov, Georgi; Raychev, Jordan; Zahariev, Plamen

doi:10.1109/telecom48729.2019.8994888

Cited by 9 publications

(5 citation statements)

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“…Artificial intelligence models for image classification and recognition could not be missing from such applications. Kinaneva D., Hristov G., Raychev J and Zahariev present in their paper an object detector for smoke and fire detection based on Faster R-CNN [34]. The corresponding ROC curve shows great results above 90%.…”

Section: Software/methodsmentioning

confidence: 97%

“…In terms of APIs, in addition to OpenGL, TensorFlow Object Detection was used in four applications [29,30,34,58] and the OpenCV library in two applications [29,50]. Of particular interest is the THEASIS system which is a stand-alone proposed platform for early detection of big wildfires and was implemented in three applications [22,30,34].…”

Section: Software/methodsmentioning

confidence: 99%

“…In addition, there were four applications with fixed-wing UAVs [29][30][31][32], while with single-rotor, it was limited to one application [33]. Finally, one application [34] was implemented with 2 UAVs, one fixed wing and one quadcopter.…”

Section: Research Early Statisticsmentioning

confidence: 99%

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Computer Vision for Fire Detection on UAVs—From Software to Hardware

2021

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Fire hazard is a condition that has potentially catastrophic consequences. Artificial intelligence, through Computer Vision, in combination with UAVs has assisted dramatically to identify this risk and avoid it in a timely manner. This work is a literature review on UAVs using Computer Vision in order to detect fire. The research was conducted for the last decade in order to record the types of UAVs, the hardware and software used and the proposed datasets. The scientific research was executed through the Scopus database. The research showed that multi-copters were the most common type of vehicle and that the combination of RGB with a thermal camera was part of most applications. In addition, the trend in the use of Convolutional Neural Networks (CNNs) is increasing. In the last decade, many applications and a wide variety of hardware and methods have been implemented and studied. Many efforts have been made to effectively avoid the risk of fire. The fact that state-of-the-art methodologies continue to be researched, leads to the conclusion that the need for a more effective solution continues to arouse interest.

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Section: Software/methodsmentioning

confidence: 97%

Section: Software/methodsmentioning

confidence: 99%

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Computer Vision for Fire Detection on UAVs—From Software to Hardware

2021

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“…A forest fire is a highly hazardous natural disaster that significantly impacts the ecosystem [1]. It not only devastates vast forest ecosystems, but also leads to an irreversible loss of biodiversity and to soil damage [2], disrupting the ecological balance. Additionally, forest fires can cause destruction to surrounding buildings, crops, and infrastructure, severely impacting economic development [3,4].…”

Section: Introductionmentioning

confidence: 99%

FFYOLO: A Lightweight Forest Fire Detection Model Based on YOLOv8

Yun,

Zheng,

Lin

et al. 2024

Fire

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Forest is an important resource for human survival, and forest fires are a serious threat to forest protection. Therefore, the early detection of fire and smoke is particularly important. Based on the manually set feature extraction method, the detection accuracy of the machine learning forest fire detection method is limited, and it is unable to deal with complex scenes. Meanwhile, most deep learning methods are difficult to deploy due to high computational costs. To address these issues, this paper proposes a lightweight forest fire detection model based on YOLOv8 (FFYOLO). Firstly, in order to better extract the features of fire and smoke, a channel prior dilatation attention module (CPDA) is proposed. Secondly, the mixed-classification detection head (MCDH), a new detection head, is designed. Furthermore, MPDIoU is introduced to enhance the regression and classification accuracy of the model. Then, in the Neck section, a lightweight GSConv module is applied to reduce parameters while maintaining model accuracy. Finally, the knowledge distillation strategy is used during training stage to enhance the generalization ability of the model and reduce the false detection. Experimental outcomes demonstrate that, in comparison to the original model, FFYOLO realizes an mAP0.5 of 88.8% on a custom forest fire dataset, which is 3.4% better than the original model, with 25.3% lower parameters and 9.3% higher frames per second (FPS).

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“…Srinivas et al [26] proposed the application of a basic CNN (Convolutional Neural Networks) architecture for classifying forest fire images, achieving a classification accuracy of 95%. Kinaneva et al [27] and Barmpoutis et al [24] used the Faster R-CNN algorithm to detect smoke and flames in UAV images. Jiao et al [28,29] proposed modified versions of YOLOv3-tiny and YOLOv3 for the real-time detection of flames and smoke in drone images.…”

Section: Introductionmentioning

confidence: 99%

An Improved YOLOv5s-Seg Detection and Segmentation Model for the Accurate Identification of Forest Fires Based on UAV Infrared Image

Niu,

Wang,

et al. 2023

Remote Sensing

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With the influence of climate change and human activities, the frequency and scale of forest fires have been increasing continuously, posing a significant threat to the environment and human safety. Therefore, rapid and accurate forest fire detection has become essential for effective control of forest fires. This study proposes a Forest Fire Detection and Segmentation Model (FFDSM) based on unmanned aerial vehicle (UAV) infrared images to address the problems of forest fire occlusion and the poor adaptability of traditional forest fire detection methods. The FFDSM integrates the YOLO (You Only Look Once) v5s-seg, Efficient Channel Attention (ECA), and Spatial Pyramid Pooling Fast Cross-Stage Partial Channel (SPPFCSPC) to improve the detection accuracy of forest fires of different sizes. The FFDSM enhances the detection and extraction capabilities of forest fire features, enabling the accurate segmentation of forest fires of different sizes and shapes. Furthermore, we conducted ablation and controlled experiments on different attention mechanisms, spatial pyramid pooling (SPP) modules, and fire sizes to verify the effectiveness of the added modules and the adaptability of the FFDSM model. The results of the ablation experiment show that, compared to the original YOLOv5s-seg model, the models fused with the ECA and SPPFCSPC achieve an improved accuracy, with FFDSM showing the greatest improvement. FFDSM achieves a 2.1% increase in precision, a 2.7% increase in recall, a 2.3% increase in mAP@0.5, and a 4.2% increase in mAP@0.5:0.95. The results of the controlled experiments on different attention mechanisms and SPP modules demonstrate that the ECA+SPPFCSPC model (FFDSM) performs the best, with a precision, recall, mAP@0.5, and mAP@0.5:0.95 reaching 0.959, 0.870, 0.907, and 0.711, respectively. The results of the controlled experiment on different fire sizes show that FFDSM outperforms YOLOv5s-seg for all three fire sizes, and it performs the best for small fires, with a precision, recall, mAP@0.5, and mAP@0.5:0.95 reaching 0.989, 0.938, 0.964, and 0.769, respectively, indicating its good adaptability for early forest fire detection. The results indicate that the forest fire detection model based on UAV infrared images (FFDSM) proposed in this study exhibits a high detection accuracy. It is proficient in identifying obscured fires in optical images and demonstrates good adaptability in various fire scenarios. The model effectively enables real-time detection and provides early warning of forest fires, providing valuable support for forest fire prevention and scientific decision making.

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Application of Artificial Intelligence in UAV platforms for Early Forest Fire Detection

Cited by 9 publications

References 0 publications

Computer Vision for Fire Detection on UAVs—From Software to Hardware

Computer Vision for Fire Detection on UAVs—From Software to Hardware

FFYOLO: A Lightweight Forest Fire Detection Model Based on YOLOv8

An Improved YOLOv5s-Seg Detection and Segmentation Model for the Accurate Identification of Forest Fires Based on UAV Infrared Image

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