Smoke detection and trend prediction method based on Deeplabv3+ and generative adversarial network

Cheng, Shuhong; Ma, Jiyong; Zhang, Shijun

doi:10.1117/1.jei.28.3.033006

Cited by 27 publications

(10 citation statements)

References 13 publications

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“…They also created synthetic smoke images instead of labeling the real smoke images manually for training and then tested the network on both synthetic and real videos. Cheng et al [ 68 ] proposed a smoke detection model using Deeplabv3+ and a generative adversarial network (GAN). The smoke pixels were first identified by fusing the result Deeplabv3+ and the heatmap of smoke based on HSV features.…”

Section: Early Fire Detection Systemsmentioning

confidence: 99%

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

Barmpoutis

Papaioannou

Dimitropoulos

et al. 2020

Sensors

309

106

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The environmental challenges the world faces nowadays have never been greater or more complex. Global areas covered by forests and urban woodlands are threatened by natural disasters that have increased dramatically during the last decades, in terms of both frequency and magnitude. Large-scale forest fires are one of the most harmful natural hazards affecting climate change and life around the world. Thus, to minimize their impacts on people and nature, the adoption of well-planned and closely coordinated effective prevention, early warning, and response approaches are necessary. This paper presents an overview of the optical remote sensing technologies used in early fire warning systems and provides an extensive survey on both flame and smoke detection algorithms employed by each technology. Three types of systems are identified, namely terrestrial, airborne, and spaceborne-based systems, while various models aiming to detect fire occurrences with high accuracy in challenging environments are studied. Finally, the strengths and weaknesses of fire detection systems based on optical remote sensing are discussed aiming to contribute to future research projects for the development of early warning fire systems.

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Section: Early Fire Detection Systemsmentioning

confidence: 99%

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

Barmpoutis

Papaioannou

Dimitropoulos

et al. 2020

Sensors

309

106

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“…Several works classify the fire and non-fire region from the image dataset [10,22]. It is broadly categorized into two groups: fire detection (flame detection) [23][24][25] and early fire detection (smoke detection) [26][27][28]. The former detection is challenging, as the smoke persists for hours even after the fire stops, thus making real fire detection more complex, with acquired images or on-site.…”

Section: Related Workmentioning

confidence: 99%

Assessing the Impact of the Loss Function and Encoder Architecture for Fire Aerial Images Segmentation Using Deeplabv3+

Harkat

Nascimento

Bernardino³

et al. 2022

Remote Sensing

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Wildfire early detection and prevention had become a priority. Detection using Internet of Things (IoT) sensors, however, is expensive in practical situations. The majority of present wildfire detection research focuses on segmentation and detection. The developed machine learning models deploy appropriate image processing techniques to enhance the detection outputs. As a result, the time necessary for data processing is drastically reduced, as the time required rises exponentially with the size of the captured pictures. In a real-time fire emergency, it is critical to notice the fire pixels and warn the firemen as soon as possible to handle the problem more quickly. The present study addresses the challenge mentioned above by implementing an on-site detection system that detects fire pixels in real-time in the given scenario. The proposed approach is accomplished using Deeplabv3+, a deep learning architecture that is an enhanced version of an existing model. However, present work fine-tuned the Deeplabv3 model through various experimental trials that have resulted in improved performance. Two public aerial datasets, the Corsican dataset and FLAME, and one private dataset, Firefront Gestosa, were used for experimental trials in this work with different backbones. To conclude, the selected model trained with ResNet-50 and Dice loss attains a global accuracy of 98.70%, a mean accuracy of 89.54%, a mean IoU 86.38%, a weighted IoU of 97.51%, and a mean BF score of 93.86%.

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“…Among various quantitative indicators, the relative error rate of the predicted value of crack length is the lowest. S. Cheng [ 13 ] used the DeepLab v 3+ to segment smoke images. U. Verma [ 14 ]et al used DeepLab v 3+ for river identification and width measurement.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Tracking of Object Melting Based on Enhanced DeepLab $v 3 +$ Network

Jiang

Dai

et al. 2022

Computational Intelligence and Neuroscience

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In order to reveal the dissolution behavior of iron tailings in blast furnace slag, the main component of iron tailings, SiO2, was used for research. Aiming at the problem of information loss and inaccurate extraction of tracking molten SiO2 particles in high temperature, a method based on the improved DeepLab v 3+ network was proposed to track, segment, and extract small object particles in real time. First, by improving the decoding layer of the DeepLab v 3+ network, construct dense ASPP (atrous spatial pyramid pooling) modules with different dilation rates to optimize feature extraction, increase the shallow convolution of the backbone network, and merge it into the upper convolution decoding part to increase detailed capture. Secondly, integrate the lightweight network MobileNet v3 to reduce network parameters, further speed up image detection, and reduce the memory usage to achieve real-time image segmentation and adapt to low-level configuration hardware. Finally, improve the expression of the loss function for the binary classification model of small object in this paper, combining the advantages of the Dice Loss binary classification segmentation and the Focal Loss balance of positive and negative samples, solving the problem of unbalanced dataset caused by the small proportion of positive samples. Experimental results show that MIoU (mean intersection over union) of the proposed model for small object segmentation is 6% higher than that of the original model, the overall MIoU is increased by 3%, and the execution time and memory consumption are only half of the original model, which can be well applied to real-time tracking and segmentation of small particles.

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Smoke detection and trend prediction method based on Deeplabv3+ and generative adversarial network

Cited by 27 publications

References 13 publications

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

Assessing the Impact of the Loss Function and Encoder Architecture for Fire Aerial Images Segmentation Using Deeplabv3+

Real-Time Tracking of Object Melting Based on Enhanced DeepLab $v 3 +$ Network

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Smoke detection and trend prediction method based on Deeplabv3+ and generative adversarial network

Cited by 27 publications

References 13 publications

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing

Assessing the Impact of the Loss Function and Encoder Architecture for Fire Aerial Images Segmentation Using Deeplabv3+

Real-Time Tracking of Object Melting Based on Enhanced DeepLabv3+Network

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Real-Time Tracking of Object Melting Based on Enhanced DeepLab $v 3 +$ Network