Real-Time Video Fire Detection via Modified YOLOv5 Network Model

Wu, Zongsheng; Ren, Xue; Li, Hong

doi:10.1007/s10694-022-01260-z

Cited by 40 publications

(11 citation statements)

References 33 publications

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“…Wang et al [ 28 ] proposed a lightweight detector, Light-YOLOv4, which considers the balance between performance and efficiency and has good detection performance and speed in embedded scenarios. Wu [ 29 ] et al improved the SPP module and activation function of YOLOv5 to improve the robustness and reliability of fire detection. Xue [ 30 ] et al introduced the convolutional block attention module (CBAM) and bidirectional feature pyramid network (BiFPN) into YOLOv5, which improved the detection of small targets in forest fires.…”

Section: Related Workmentioning

confidence: 99%

Fire Detection in Ship Engine Rooms Based on Deep Learning

Zhu

Zhang

Wang

et al. 2023

Sensors

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Ship fires are one of the main factors that endanger the safety of ships; because the ship is far away from land, the fire can be difficult to extinguish and could often cause huge losses. The engine room has many pieces of equipment and is the principal place of fire; however, due to its complex internal environment, it can bring many difficulties to the task of fire detection. The traditional detection methods have their own limitations, but fire detection using deep learning technology has the characteristics of high detection speed and accuracy. In this paper, we improve the YOLOv7-tiny model to enhance its detection performance. Firstly, partial convolution (PConv) and coordinate attention (CA) mechanisms are introduced into the model to improve its detection speed and feature extraction ability. Then, SIoU is used as a loss function to accelerate the model’s convergence and improve accuracy. Finally, the experimental results on the dataset of the ship engine room fire made by us shows that the mAP@0.5 of the improved model is increased by 2.6%, and the speed is increased by 10 fps, which can meet the needs of engine room fire detection.

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Section: Related Workmentioning

confidence: 99%

Fire Detection in Ship Engine Rooms Based on Deep Learning

Zhu

Zhang

Wang

et al. 2023

Sensors

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“…Therefore, flame detection techniques are employed for safety monitoring in power transmission scenarios. Current flame detection tasks [23], [24], [25] primarily focus on normal weather conditions and lack relevant research on foggy flame detection. Chen et al [12] explored the speed issue of flame detection models based on YOLOv5 (you only look once version 5).…”

Section: B Flame Detection Taskmentioning

confidence: 99%

Dehazing Model of Extra-High Voltage Converter Station Based on Two-Stage Attention

Rui,

Jiaqing,

Yang

2023

IEEE Access

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Fire detection is an important technology to reliably guarantee the safety of power transmission scenarios. An effective fire detection model can help fire-fighting robots be deployed in a timely and accurate manner. However, under hazy conditions, the fire information in the image will appear blurred, resulting in a decrease in the accuracy of the fire detection model. In response to this problem, this paper designs a hazy fire detection enhancement model based on two-stage attention and fusion loss. First, the visual multihead self-attention model is used to extract fire features in the first stage, and then through the channel dimension and spatial dimension attention as the reconstruction augmentation module for the second stage. The color loss is computed by color space conversion and combined with structural information restored loss. The dehazing and fire detection experiments are carried out on the fire dataset. The fire image restoration evaluation metrics and fire detection evaluation metrics are calculated. The experimental results verified the promotion and strengthening effect of the proposed dehazing model on the fire detection task.

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“…This method is a variant of the pooling operation, which prevents information loss as much as possible during the pooling process and is more friendly to the detection of small targets. Zongsheng Wu et al [52] introduced atrous convolutions in the SPP module to improve the detection of small objects. Atrous convolutions with kernel sizes of 3 × 3 and dilation-rate sizes of 2, 5, and 9 are added after the max-pooling layers with pooling kernel sizes of 3, 5, and 9, respectively.…”

Section: Improvements Made To the Spp Modulementioning

confidence: 99%

Target Detection Method of UAV Aerial Imagery Based on Improved YOLOv5

Luo

Wang

2022

Remote Sensing

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Due to the advantages of small size, lightweight, and simple operation, the unmanned aerial vehicle (UAV) has been widely used, and it is also becoming increasingly convenient to capture high-resolution aerial images in a variety of environments. Existing target-detection methods for UAV aerial images lack outstanding performance in the face of challenges such as small targets, dense arrangement, sparse distribution, and a complex background. In response to the above problems, some improvements on the basis of YOLOv5l have been made by us. Specifically, three feature-extraction modules are proposed, using asymmetric convolutions. They are named the Asymmetric ResNet (ASResNet) module, Asymmetric Enhanced Feature Extraction (AEFE) module, and Asymmetric Res2Net (ASRes2Net) module, respectively. According to the respective characteristics of the above three modules, the residual blocks in different positions in the backbone of YOLOv5 were replaced accordingly. An Improved Efficient Channel Attention (IECA) module was added after Focus, and Group Spatial Pyramid Pooling (GSPP) was used to replace the Spatial Pyramid Pooling (SPP) module. In addition, the K-Means++ algorithm was used to obtain more accurate anchor boxes, and the new EIOU-NMS method was used to improve the postprocessing ability of the model. Finally, ablation experiments, comparative experiments, and visualization of results were performed on five datasets, namely CIFAR-10, PASCAL VOC, VEDAI, VisDrone 2019, and Forklift. The effectiveness of the improved strategies and the superiority of the proposed method (YOLO-UAV) were verified. Compared with YOLOv5l, the backbone of the proposed method increased the top-one accuracy of the classification task by 7.20% on the CIFAR-10 dataset. The mean average precision (mAP) of the proposed method on the four object-detection datasets was improved by 5.39%, 5.79%, 4.46%, and 8.90%, respectively.

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Real-Time Video Fire Detection via Modified YOLOv5 Network Model

Cited by 40 publications

References 33 publications

Fire Detection in Ship Engine Rooms Based on Deep Learning

Fire Detection in Ship Engine Rooms Based on Deep Learning

Dehazing Model of Extra-High Voltage Converter Station Based on Two-Stage Attention

Target Detection Method of UAV Aerial Imagery Based on Improved YOLOv5

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