Development and optimization of image fire detection on deep learning algorithms

Yang, Yi; Pan, Mengyi; Li, Pu; Wang, Xuefeng; Tsai, Yun‐Ting

doi:10.1007/s10973-022-11657-1

Cited by 9 publications

(2 citation statements)

References 26 publications

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“…Yang et al [37] modified the YOLOv3 model for the detection of smoke and wildfires. To improve the detection of small fires and smoke, an additional large-scale feature map is used for the neck.…”

Section: Related Workmentioning

confidence: 99%

YOLO-Based Models for Smoke and Wildfire Detection in Ground and Aerial Images

Gonçalves,

Ghali,

Akhloufi

2024

Fire

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Wildland fires negatively impact forest biodiversity and human lives. They also spread very rapidly. Early detection of smoke and fires plays a crucial role in improving the efficiency of firefighting operations. Deep learning techniques are used to detect fires and smoke. However, the different shapes, sizes, and colors of smoke and fires make their detection a challenging task. In this paper, recent YOLO-based algorithms are adopted and implemented for detecting and localizing smoke and wildfires within ground and aerial images. Notably, the YOLOv7x model achieved the best performance with an mAP (mean Average Precision) score of 80.40% and fast detection speed, outperforming the baseline models in detecting both smoke and wildfires. YOLOv8s obtained a high mAP of 98.10% in identifying and localizing only wildfire smoke. These models demonstrated their significant potential in handling challenging scenarios, including detecting small fire and smoke areas; varying fire and smoke features such as shape, size, and colors; the complexity of background, which can include diverse terrain, weather conditions, and vegetation; and addressing visual similarities among smoke, fog, and clouds and the the visual resemblances among fire, lighting, and sun glare.

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Section: Related Workmentioning

confidence: 99%

YOLO-Based Models for Smoke and Wildfire Detection in Ground and Aerial Images

Gonçalves,

Ghali,

Akhloufi

2024

Fire

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“…The work in Ref. [9] developed an image-based fire detection algorithm based on the YOLO-V3 network to detect smoke and flames. Despite their success, most previous studies focused on model optimization, and the complex situations in real environments need to be considered.…”

Section: Introductionmentioning

confidence: 99%

Anchor-Free Smoke and Flame Recognition Algorithm with Multi-Loss

Chen

et al. 2023

Fire

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Fire perception based on machine vision is essential for improving social safety. Object recognition based on deep learning has become the mainstream smoke and flame recognition method. However, the existing anchor-based smoke and flame recognition algorithms are not accurate enough for localization due to the irregular shapes, unclear contours, and large-scale changes in smoke and flames. For this problem, we propose a new anchor-free smoke and flame recognition algorithm, which improves the object detection network in two dimensions. First, we propose a channel attention path aggregation network (CAPAN), which forces the network to focus on the channel features with foreground information. Second, we propose a multi-loss function. The classification loss, the regression loss, the distribution focal loss (DFL), and the loss for the centerness branch are fused to enable the network to learn a more accurate distribution for the locations of the bounding boxes. Our method attains a promising performance compared with the state-of-the-art object detectors; the recognition accuracy improves by 5% for the mAP, 8.3% for the flame AP50, and 2.1% for the smoke AP50 compared with the baseline model. Overall, the algorithm proposed in this paper significantly improves the accuracy of the object detection network in the smoke and flame recognition scenario and can provide real-time fire recognition.

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Investigation on incompatible hazards of nitrocellulose mixed with three types of copper compounds

Yang

Yuan

et al. 2023

J Therm Anal Calorim

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Development and optimization of image fire detection on deep learning algorithms

Cited by 9 publications

References 26 publications

YOLO-Based Models for Smoke and Wildfire Detection in Ground and Aerial Images

YOLO-Based Models for Smoke and Wildfire Detection in Ground and Aerial Images

Anchor-Free Smoke and Flame Recognition Algorithm with Multi-Loss

Investigation on incompatible hazards of nitrocellulose mixed with three types of copper compounds

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