Urban Firefighting Drones: Precise Throwing from UAV

Perez-Saura, David; Fernandez-Cortizas, Miguel; Perez-Segui, Rafael; Arias-Perez, Pedro; Campoy, Pascual

doi:10.1007/s10846-023-01883-6

Cited by 7 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, deep learning technology has shown immense potential in forest fire detection. Its efficient image processing and pattern recognition capabilities are particularly suited for analyzing large volumes of remote sensing data [8], such as satellite imagery and photos taken by drones [9], which are crucial for monitoring and responding to forest fires. Satellite images offer a wide-range monitoring perspective and are an important tool for quickly assessing the extent and impact of fires.…”

Section: Introductionmentioning

confidence: 99%

“…Current research in this field is still in its early stages, partly due to the relatively limited application of advanced technologies like semantic segmentation and instance segmentation in forest fire detection [15]. The high precision requirements of these technologies demand higher standards of data quality and also pose challenges to the performance and generalization capabilities of models [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. Given that the identification of the fire front is crucial for early warning systems and the formulation of firefighting strategies, developing technologies that can more precisely identify and analyze the fire front has become an urgent need in this field [17].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fire in Focus: Advancing Wildfire Image Segmentation by Focusing on Fire Edges

Wang,

Zhou

et al. 2024

Forests

View full text Add to dashboard Cite

With the intensification of global climate change and the frequent occurrence of forest fires, the development of efficient and precise forest fire monitoring and image segmentation technologies has become increasingly important. In dealing with challenges such as the irregular shapes, sizes, and blurred boundaries of flames and smoke, traditional convolutional neural networks (CNNs) face limitations in forest fire image segmentation, including flame edge recognition, class imbalance issues, and adapting to complex scenarios. This study aims to enhance the accuracy and efficiency of flame recognition in forest fire images by introducing a backbone network based on the Swin Transformer and combined with an adaptive multi-scale attention mechanism and focal loss function. By utilizing a rich and diverse pre-training dataset, our model can more effectively capture and understand key features of forest fire images. Through experimentation, our model achieved an intersection over union (IoU) of 86.73% and a precision of 91.23%. This indicates that the performance of our proposed wildfire segmentation model has been effectively enhanced. A series of ablation experiments validate the importance of these technological improvements in enhancing model performance. The results show that our approach achieves significant performance improvements in forest fire image segmentation tasks compared to traditional models. The Swin Transformer provides more refined feature extraction capabilities, the adaptive multi-scale attention mechanism helps the model focus better on key areas, and the focal loss function effectively addresses the issue of class imbalance. These innovations make the model more precise and robust in handling forest fire image segmentation tasks, providing strong technical support for future forest fire monitoring and prevention.

show abstract

Section: Introductionmentioning

confidence: 99%