Flame and Smoke Detection Algorithm for UAV Based on Improved YOLOv4-Tiny

Wu, Ruinan; Chen, Hua; Ding, Weili; Wang, Yifan; Wang, Yubao

doi:10.1007/978-3-030-89188-6_17

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…The improved YOLOv5s network model improves the AP value by 0.9%, the P reaches 94.7%, and the detection speed reaches 65.4 frames/s. Compared with a series of YOLO algorithms, as YOLOv4 [34] and YOLOv4‐tiny [35], the AP on this dataset is improved by 1.3% and 5.3%, respectively. Compared with some advanced algorithms such as Faster RCNN [36], DeepSmoke [19] and SSD [37], the improved model in this paper performs well.…”

Section: Resultsmentioning

confidence: 99%

A novel smoke detection algorithm based on improved mixed Gaussian and YOLOv5 for textile workshop environments

Chen

Xue

Zhu

et al. 2023

IET Image Processing

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There are a lot of flammable materials in the textile workshop, and once a fire occurs, it will cause property damage and casualties. At present, smoke detection in textile workshops mainly relies on temperature‐sensing smoke sensors with low detection rate and poor real‐time performance, which cannot meet the task of smoke detection in complex environments. Therefore, this paper proposes an improved mixed Gaussian and YOLOv5 smoke detection algorithm for textile workshops. In order to reduce the interference of static background in smoke detection, an improved gaussian mixture algorithm is used to extract suspected smoke areas in video by using the dynamic characteristics of smoke. Then, an adaptive attention module is added to the feature pyramid infrastructure of the YOLOv5 target detection network to improve the multi‐scale target recognition ability. In addition, the focal loss function is used to reduce the impact of background and foreground class imbalances on the detection results. The experimental results show that the detection accuracy of the proposed method is 94.7%, and the average detection speed is 66.7 FPS. By comparing with the existing state‐of‐the‐art algorithms, the detection capability of this method has been significantly improved. At the same time, it has high real‐time performance and detection accuracy in smoke detection in textile workshops.

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Section: Resultsmentioning

confidence: 99%

A novel smoke detection algorithm based on improved mixed Gaussian and YOLOv5 for textile workshop environments

Chen

Xue

Zhu

et al. 2023

IET Image Processing

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“…Attention mechanisms have shown excellent performance in a wide range of computer vision tasks, such as flame detection [28]. We find recent studies only focus on channel or spatial attentions for flame detection [29–32].…”

Section: Methodsmentioning

confidence: 99%

Dynamic attention network for flame detection

Fang

Zhang

et al. 2023

Electronics Letters

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Recently, flame detection has attracted great attention. However, existing methods have the issues of low detection rates, high false alarm rates, and lack of smoke anti-interference ability. In this letter, a novel dynamic attention-based network (DANet) is proposed for autonomous flame detection in various scenarios. To mitigate the disturbance of smoke in images, a dynamic attention strategy is proposed to discover the potential features among scale-awareness and spatial-awareness. Then, based on dynamic attention module, a decoupled detection head is presented, which can predict category, regression, and object score independently to boost the performance. A self-contained challenging flame dataset, which is multi-scene, multiscale, and multi-interference informative is constructed to evaluate the proposed model and organize the experiments. Extensive ablation and comparison studies on self-labelled dataset reveal the effectiveness of the proposed dynamic attention-based network.

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“…Following that, the YOLO series has been developed over many upgraded versions. There are many flame detection algorithms based on YOLOv3, YOLOv4, and YOLOv5 [5][6][7][8][9]. YOLOv7 [15] is the latest version of the YOLO series, and in [16], it is proved that the performance of YOLOv7 is obviously better than YOLOv5 and YOLOv6 in the aspect of target detection.…”

Section: Background On Target Detectionmentioning

confidence: 99%

“…Convolutional neural networks have strong learning ability, fault tolerance, and fast speed; thus, they are commonly used in image recognition and classification. Currently, the convolutional neural networks (CNNs) used for object detection mainly include region-convolutional neural networks (R-CNN) [4] and YOLO series [5][6][7][8][9]. Compared with other convolutional neural networks, the YOLO series can better extract global information from images and can be trained end-to-end, which assures them as a more suitable option for flame detection.…”

Section: Introductionmentioning

confidence: 99%

A Flame Detection Algorithm Based on Improved YOLOv7

et al. 2023

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Flame recognition is of great significance in fire prevention. However, current algorithms for flame detection have some problems, such as missing detection and false detection, and the detection accuracy cannot satisfy the requirements for fire prevention. In order to further the above problems, we propose a flame detection algorithm based on an improved YOLOv7 network. In our algorithm, we replace a convolution of the MP-1 module with a SimAM structure, which is a parameter-free attention mechanism. In this way, the missing detection problem can be improved. Furthermore, we use a ConvNeXt-based CNeB module to replace a convolution of the ELAN-W module for increasing detection accuracy and the false detection problem in complex environments. Finally, we evaluate the performance of our algorithm through a large number of test cases, and the data set used in our experiments was constructed by combining several publicly available data sets for various application scenarios. The experimental results indicate that compared with the original YOLOv7 algorithm, our proposed algorithm can achieve a 7% increase in the aspect of mAP_0.5 and a 4.1% increase in the aspect of F1 score.

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Flame and Smoke Detection Algorithm for UAV Based on Improved YOLOv4-Tiny

Cited by 4 publications

References 16 publications

A novel smoke detection algorithm based on improved mixed Gaussian and YOLOv5 for textile workshop environments

A novel smoke detection algorithm based on improved mixed Gaussian and YOLOv5 for textile workshop environments

Dynamic attention network for flame detection

A Flame Detection Algorithm Based on Improved YOLOv7

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