Machine Vision Based Fire Detection Techniques: A Survey

Geetha, S.; Abhishek, C.; Akshayanat, C. S.

doi:10.1007/s10694-020-01064-z

Cited by 80 publications

(36 citation statements)

References 59 publications

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“…During the shooting process, the player's movement may be determined based on the object's movement trajectory, and the player's picture can be adjusted in this region, removing the camera's movement. [20]. In the expanded tracking object, through symmetrical, vertical, and horizontal tracking, the image of the moving object is obtained, and the corresponding correction is made.…”

Section: Action Correction Of Long-distance Running Sportsmentioning

confidence: 99%

Convolutional Neural Network and Computer Vision-Based Action Correction Method for Long-Distance Running Technology

Wang

2022

Security and Communication Networks

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For long-distance running tactics, a computer vision-based motion correction solution is presented. The depth information is merged into the KCF algorithm to enhance it, which overcomes the classic KCF method’s incapacity to tackle the tracking drift issue caused by occlusion and extracts the technical features of long-distance running motions. Based on computer vision, the posture area of long-distance runners is detected, and the technical movements of long-distance runners are recognized. Calculate the centroid coordinates of the wrong technical movement correction area in long-distance running, and generate a tracking image of the wrong technical movement in long-distance running. The foreground and background information of the image is separated by the optical flow feature of machine vision, and the motion trajectory of the long-distance running error technique is extracted, and the motion correction of the long-distance running technique based on computer vision is realized. The experimental results show that the method in this paper has better accuracy in extracting long-distance running motion features and can accurately identify and correct the technical movements of the long-distance running, the technical movements of the head, and the technical movements of the body balance, and the correction efficiency is high.

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Section: Action Correction Of Long-distance Running Sportsmentioning

confidence: 99%

Convolutional Neural Network and Computer Vision-Based Action Correction Method for Long-Distance Running Technology

Wang

2022

Security and Communication Networks

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“…The approach improves feature extraction and classification performance [11]. Fire detection has been enabled in surveillance equipment and accuracy will be good [20]. When the fire is detected in any camera, the deep learning approach can identify it and send a signal or an alert message, and then the responsible person can be alerted in terms of fire control and damage prevention.…”

Section: Deep Learningmentioning

confidence: 99%

Real-time fire detection based on CNN and inception V3 algorithms

Bhavani¹,

Iswarya²,

Lokesh³

2022

ijhs

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Fire is an abnormal event that can cause significant damage to lives and property within a very short time. The main cause of fire disaster includes a human error or system failure which results in severe loss of human life and other damages. Traditional fire alarms are based on sensors that require proximity for activation. They need human involvement to confirm a fire. To overcome these limitations, vision-based real-time fire detection has been enabled in surveillance devices. Once fire appears in any camera, the approach can detect it and send a signal to respective officers of the fire region. This work focused on an intelligent approach using the Deep Learning model for preventing fire hazards from going out of control in high-fire-risk areas. Deep Learning models are effective for fire detection. Convolutional Neural Networks outperform other algorithms in terms of accuracy. In this work Convolution, Neural Network model Inception V3 is used to detect fire indoors and outdoors and protect the surroundings and living beings.

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“…In computer vision applications, the human vision mechanism is imitated. In this way, object detection on images or information about the image can be obtained [37]. The human eye performs its optimum vision function by adjusting itself to external environmental conditions.…”

Section: Image Processingmentioning

confidence: 99%

Fire Detection inImages Using FrameworkBased on Image Processing, Motion Detection and Convolutional Neural Network

Taşpınar¹,

Köklü²,

Altin³

2021

ijisae

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Fire detection in images has been frequently used recently to detect fire at an early stage. These methods play an important role in reducing the loss of life and property. Fire is not only chemically complex, but also physically very complex. The shape and color of the flame varies according to the type of fuel in the fire. This has made fire detection a very challenging problem. Advanced image processing algorithms are also needed to accurately detect fire. To solve this problem, a three-stage fire framework was created in this study. In the first stage, the flame region was extracted from the images containing the fire region with the basic image processing algorithms. At this stage, reduce brightness, HSL, YCbCr, median and herbaceous filters are applied successively to the image. Since the flame image has a polygonal structure by nature, the number of edges of the flame region has been found. In the second stage, the mobility feature of the flame was utilized. For this purpose, the mobility of the flame was determined by comparing the video frames containing the fire image. The CNN method was used to detect the fire in the images. The CNN model was trained with the transfer learning method using the Inception V3, SequeezeNet, VGG16 and VGG19 trained models. As a result of the tests of the models, 98.8%, 97.0%, 97.3% and 96.8% classification success were obtained, respectively. With the proposed fire detection framework, it is thought that the damage caused by the fire can be reduced by early detection of the fire and timely intervention.

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Machine Vision Based Fire Detection Techniques: A Survey

Cited by 80 publications

References 59 publications

Convolutional Neural Network and Computer Vision-Based Action Correction Method for Long-Distance Running Technology

Convolutional Neural Network and Computer Vision-Based Action Correction Method for Long-Distance Running Technology

Real-time fire detection based on CNN and inception V3 algorithms

Fire Detection inImages Using FrameworkBased on Image Processing, Motion Detection and Convolutional Neural Network

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