Study of Flame Detection based on Improved YOLOv4

Cao, Chengzhi; Tan, Xiaoyu; Huang, Xinyi; Zhang, Yongjun; Luo, Zehao

doi:10.1088/1742-6596/1952/2/022016

Cited by 7 publications

(4 citation statements)

References 2 publications

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“…Testing the model achieves a class-balanced accuracy of 78.32%. S.Natesan et al [19][20][21][22][23] proposed a new method of UAV monitoring tree species based on residual neural network, using the images collected by UAV in the past three years to train the artificial neural network, respectively conducted two groups of experiments, obtained 80% and 51% tree species classification accuracy. Franklin et al proposed a new method for UAV tree species classification based on object image analysis and machine learning, using image segmentation technology to segment the acquired images.…”

Section: Introductionmentioning

confidence: 99%

Detection of Pine Wilt Nematode from Drone Images Using UAV

Sun

Ibrayim

Hamdulla

2022

Sensors

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Pine wilt nematode disease is a devastating forest disease that spreads rapidly. Using drone remote sensing to monitor pine wilt nematode trees promptly is an effective way to control the spread of pine wilt nematode disease. In this study, the YOLOv4 algorithm was used to automatically identify abnormally discolored wilt from pine wilt nematode disease on UAV remote sensing images. Because the network structure of YOLOv4 is too complex, although the detection accuracy is high, the detection speed is relatively low. To solve this problem, the lightweight deep learning network MobileNetv2 is used to optimize the backbone feature extraction network. Furthermore, the YOLOv4 algorithm was improved by improving the backbone network part, adding CBAM attention, and adding the Inceptionv2 structure to reduce the number of model parameters and improve the accuracy and efficiency of identification. The speed and accuracy of the Faster R-CNN, YOLOv4, SSD, YOLOv5, and the improved MobileNetv2-YOLOv4 algorithm were compared, and the detection effects of the Faster R-CNN, YOLOv4, SSD, YOLOv5 and the improved MobileNetv2-YOLOv4 algorithm on trees with pine wilt nematode were analyzed. The experimental results show that the average precision of the improved MobileNetv2-YOLOv4 algorithm is 86.85%, the training time of each iteration cycle is 156 s, the parameter size is 39.23 MB, and the test time of a single image is 15 ms, which is better than Faster R-CNN, YOLOv4, and SSD, but comparable to YOLOv5. Compared with the advantages and disadvantages, comprehensively comparing these four indicators, the improved algorithm has a more balanced performance in the detection speed, the parameter size, and the average precision. The F1 score of the improved algorithm (95.60%) was higher than that of Faster R-CNN (90.80%), YOLOv4 (94.56%), and SSD (92.14%), which met the monitoring requirements of pine wilt nematode trees. Faster R-CNN and SSD pine-wilt-nematode tree detection models are not ideal in practical applications. Compared with the YOLOv4 pine-wilt-nematode tree detection model, the improved MobileNetv2-YOLOv4 algorithm satisfies the condition of maintaining a lower model parameter quantity to obtain higher detection accuracy; therefore, it is more suitable for practical application scenarios of embedded devices. It can be used for the rapid detection of pine wilt nematode diseased trees.

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Section: Introductionmentioning

confidence: 99%

Detection of Pine Wilt Nematode from Drone Images Using UAV

Sun

Ibrayim

Hamdulla

2022

Sensors

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“…The algorithm improves the recognition accuracy and detection speed of small target smoke by improving the prediction frame size of K-means clustering algorithm in YOLOv3. Cao et al (2021) proposed a YOLOv4 accuracy improvement strategy based on multi-scale feature maps and made some improvements by improving the feature extraction network to detect small objects. Xue et al (2022) proposed an improved model based on YOLOv5s to improve the accuracy of small target forest fire detection by using transfer learning method for the small target size in long-distance forest fire images, which is difficult to capture effective information.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flame smoke detection algorithm based on YOLOv5 in petrochemical plant

Yang

Shou-song

et al. 2023

IJICC

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PurposeFire smoke detection in petrochemical plant can prevent fire and ensure production safety and life safety. The purpose of this paper is to solve the problem of missed detection and false detection in flame smoke detection under complex factory background.Design/methodology/approachThis paper presents a flame smoke detection algorithm based on YOLOv5. The target regression loss function (CIoU) is used to improve the missed detection and false detection in target detection and improve the model detection performance. The improved activation function avoids gradient disappearance to maintain high real-time performance of the algorithm. Data enhancement technology is used to enhance the ability of the network to extract features and improve the accuracy of the model for small target detection.FindingsBased on the actual situation of flame smoke, the loss function and activation function of YOLOv5 model are improved. Based on the improved YOLOv5 model, a flame smoke detection algorithm with generalization performance is established. The improved model is compared with SSD and YOLOv4-tiny. The accuracy of the improved YOLOv5 model can reach 99.5%, which achieves a more accurate detection effect on flame smoke. The improved network model is superior to the existing methods in running time and accuracy.Originality/valueAiming at the actual particularity of flame smoke detection, an improved flame smoke detection network model based on YOLOv5 is established. The purpose of optimizing the model is achieved by improving the loss function, and the activation function with stronger nonlinear ability is combined to avoid over-fitting of the network. This method is helpful to improve the problems of missed detection and false detection in flame smoke detection and can be further extended to pedestrian target detection and vehicle running recognition.

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“…The algorithm improves the accuracy and detection speed of small smoke targets by modifying the predicted box sizes of the K-means clustering algorithm in YOLOv3. Cao et al [9] proposed a precision enhancement strategy for YOLOv4 based on multi-scale feature maps and made improvements in detecting small objects by enhancing the feature extraction network. However, this significantly increased the algorithm complexity, resulting in a significant decrease in real-time detection.…”

Section: Introductionmentioning

confidence: 99%

Improved Model for Smoke Detection Based on Concentration Features using YOLOv7tiny

ZHENG,

Niu,

GAN

et al. 2023

IJACSA

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Smoke is often present in the early stages of a fire. Detecting low smoke concentration and small targets during these early stages can be challenging. This paper proposes an improved smoke detection algorithm that leverages the characteristics of smoke concentration using YOLOv7tiny. The improved algorithm consists of the following components: 1) utilizing the dark channel prior theory to extract smoke concentration characteristics and using the synthesized αRGB image as an input feature to enhance the features of sparse smoke; 2) designing a light-BiFPN multi-scale feature fusion structure to improve the detection performance of small target smoke; 3) using depth separable convolution to replace the original standard convolution and reduce the model parameter quantity. Experimental results on a self-made dataset show that the improved algorithm performs better in detecting sparse smoke and small target smoke, with mAP@0.5 and Recall reaching 94.03% and 95.62% respectively, and the detection FPS increasing to 118.78 frames/s. Moreover, the model parameter quantity decreases to 4.97M. The improved algorithm demonstrates superior performance in the detection of sparse and small smoke in the early stages of a fire.

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Study of Flame Detection based on Improved YOLOv4

Cited by 7 publications

References 2 publications

Detection of Pine Wilt Nematode from Drone Images Using UAV

Detection of Pine Wilt Nematode from Drone Images Using UAV

Flame smoke detection algorithm based on YOLOv5 in petrochemical plant

Improved Model for Smoke Detection Based on Concentration Features using YOLOv7tiny

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