Real-time fire detection algorithms running on small embedded devices based on MobileNetV3 and YOLOv4

Zheng, Hongtao; Duan, Jiangyong; Dong, Yu; Liu, Yan

doi:10.1186/s42408-023-00189-0

Cited by 21 publications

(7 citation statements)

References 61 publications

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“…Deconstruction experiments were designed to validate the lightweight improvement effects of the GSConv module. Figure 5 [39,40]. The GSConv module combines depthwise separable convolution with standard convolution.…”

Section: Mgsnet Modelmentioning

confidence: 99%

“…The lightweight MobileNetv3 network introduces a lightweight SE (squeeze and excitation) attention module that better extracts black smoke feature information. The inverted residual structure facilitates the flow of black smoke feature information between layers, and the h-swish activation function accelerates computation [39,40]. The GSConv module combines depthwise separable convolution with standard convolution.…”

Section: Mgsnet Modelmentioning

confidence: 99%

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A Lightweight Model for Real-Time Detection of Vehicle Black Smoke

Chen,

Wang,

Zhai

2023

Sensors

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This paper discusses the application of deep learning technology in recognizing vehicle black smoke in road traffic monitoring videos. The use of massive surveillance video data imposes higher demands on the real-time performance of vehicle black smoke detection models. The YOLOv5s model, known for its excellent single-stage object detection performance, has a complex network structure. Therefore, this study proposes a lightweight real-time detection model for vehicle black smoke, named MGSNet, based on the YOLOv5s framework. The research involved collecting road traffic monitoring video data and creating a custom dataset for vehicle black smoke detection by applying data augmentation techniques such as changing image brightness and contrast. The experiment explored three different lightweight networks, namely ShuffleNetv2, MobileNetv3 and GhostNetv1, to reconstruct the CSPDarknet53 backbone feature extraction network of YOLOv5s. Comparative experimental results indicate that reconstructing the backbone network with MobileNetv3 achieved a better balance between detection accuracy and speed. The introduction of the squeeze excitation attention mechanism and inverted residual structure from MobileNetv3 effectively reduced the complexity of black smoke feature fusion. Simultaneously, a novel convolution module, GSConv, was introduced to enhance the expression capability of black smoke features in the neck network. The combination of depthwise separable convolution and standard convolution in the module further reduced the model’s parameter count. After the improvement, the parameter count of the model is compressed to 1/6 of the YOLOv5s model. The lightweight vehicle black smoke real-time detection network, MGSNet, achieved a detection speed of 44.6 frames per second on the test set, an increase of 18.9 frames per second compared with the YOLOv5s model. The mAP@0.5 still exceeded 95%, meeting the application requirements for real-time and accurate detection of vehicle black smoke.

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Section: Mgsnet Modelmentioning

confidence: 99%

Section: Mgsnet Modelmentioning

confidence: 99%

A Lightweight Model for Real-Time Detection of Vehicle Black Smoke

Chen,

Wang,

Zhai

2023

Sensors

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“…Various studies have already employed YOLO algorithms for fire and smoke detection (Mukhiddinov et al, 2022;Zhao et al, 2022;Bahhar et al, 2023). In several cases, lighter and less processing intensive versions of YOLO architectures have achieved performance and speed suitable for real-time applications (Wang et al, 2021;Wang et al, 2022b;Zheng et al, 2023). Speed improvements were achieved by replacing the CSPDarknet backbone network with MobileNet, a lightweight convolutional neural network for mobile and embedded devices.…”

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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“…Compared with traditional image-based fire detection algorithms, deep learning technologies present a novel approach to visual-based fire detection. They have demonstrated outstanding performance in automatic feature extraction, coupled with high accuracy, enhanced speed, reliable operation, and cost-effectiveness [22][23][24][25][26]. Frizzi et al classified images as fire, smoke, and no fire using a six-layer convolutional neural network (CNN) [27].…”

Section: Introductionmentioning

confidence: 99%

An Image-Based Fire Monitoring Algorithm Resistant to Fire-like Objects

Xu,

Zhang,

Deng

et al. 2023

Fire

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Due to its wide monitoring range and low cost, visual-based fire detection technology is commonly used for fire detection in open spaces. However, traditional fire detection algorithms have limitations in terms of accuracy and speed, making it challenging to detect fires in real time. These algorithms have poor anti-interference ability against fire-like objects, such as emissions from factory chimneys, clouds, etc. In this study, we developed a fire detection approach based on an improved YOLOv5 algorithm and a fire detection dataset with fire-like objects. We added three Convolutional Block Attention Modules (CBAMs) to the head network of YOLOv5 to improve its feature extraction ability. Meanwhile, we used the C2f module to replace the original C2 module to capture rich gradient flow information. Our experimental results show that the proposed algorithm achieved a mAP@50 of 82.36% for fire detection. In addition, we also conducted a comparison test between datasets with and without labeling information for fire-like objects. Our results show that labeling information significantly reduced the false-positive detection proportion of fire-like objects incorrectly detected as fire objects. Our experimental results show that the CBAM and C2f modules enhanced the network’s feature extraction ability to differentiate fire objects from fire-like objects. Hence, our approach has the potential to improve fire detection accuracy, reduce false alarms, and be more cost-effective than traditional fire detection methods. This method can be applied to camera monitoring systems for automatic fire detection with resistance to fire-like objects.

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Real-time fire detection algorithms running on small embedded devices based on MobileNetV3 and YOLOv4

Cited by 21 publications

References 61 publications

A Lightweight Model for Real-Time Detection of Vehicle Black Smoke

A Lightweight Model for Real-Time Detection of Vehicle Black Smoke

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

An Image-Based Fire Monitoring Algorithm Resistant to Fire-like Objects

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