An efficient fire detection algorithm based on multi‐scale convolutional neural network

2021 ◽  
Author(s):  
Yanying Cheng ◽  
Ke Chen ◽  
Hui Bai ◽  
Chunjie Mou ◽  
Yuchun Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Multi Scale

scholarly journals 3D Object Detection Algorithm for Panoramic Images With Multi-Scale Convolutional Neural Network

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 171461-171470
Author(s):  
Dianwei Wang ◽  
Yanhui He ◽  
Ying Liu ◽  
Daxiang Li ◽  
Shiqian Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection Algorithm ◽  
3D Object ◽  
Multi Scale ◽  
Panoramic Images ◽  
3D Object Detection

scholarly journals A Robust and Dynamic Fire Detection Algorithm using Convolutional Neural Network

2021 ◽  
pp. 6-10
Author(s):  
Mrs. K. Sivasankari ◽  
◽  
Shubham Singh ◽  
Kanhaiya Kumar ◽  
Aman Dubey ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Major Part ◽  
Fire Detection ◽  
Data Preprocessing ◽  
Detection Algorithm ◽  
Direction Vector ◽  
Smoke Detection ◽  
Major Process

The major part of the underlying idea is going to detect the fire from upcoming smoke and the shade color of the smoke using convolutional neural network. The fire detection followed by the smoke detection is going to depend on the shade and the direction vector analysis in this paper. Image processing from the available set of data is very vague ideation so in order to strengthen the idea we are incorporating two main features that is the smoke shade and direction vector. For this major process we will involve data preprocessing through bi-variate hypothesis to select two variables as the color of smoke and the direction of the smoke and hence do the further analysis on other features that how are they going to help in the upcoming detection neurons for the robust algorithm of fire detection.

scholarly journals A Robust and Dynamic Fire Detection Algorithm using Convolutional Neural Network

2021 ◽  
Vol 1 (2) ◽  
pp. 6-10
Author(s):  
Sivasankar K. ◽  
Shubham Singh ◽  
Kanhaiya Kumar ◽  
Aman Dubey
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Major Part ◽  
Fire Detection ◽  
Data Preprocessing ◽  
Detection Algorithm ◽  
Direction Vector ◽  
Smoke Detection ◽  
Major Process

The major part of the underlying idea is going to detect the fire from upcoming smoke and the shade color of the smoke using convolutional neural network. The fire detection followed by the smoke detection is going to depend on the shade and the direction vector analysis in this paper. Image processing from the available set of data is very vague ideation so in order to strengthen the idea we are incorporating two main features that is the smoke shade and direction vector. For this major process we will involve data preprocessing through bi-variate hypothesis to select two variables as the color of smoke and the direction of the smoke and hence do the further analysis on other features that how are they going to help in the upcoming detection neurons for the robust algorithm of fire detection

scholarly journals A Method of Green Citrus Detection in Natural Environments Using a Deep Convolutional Neural Network

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhenhui Zheng ◽  
Juntao Xiong ◽  
Huan Lin ◽  
Yonglin Han ◽  
Baoxia Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Performance ◽  
Visual Detection ◽  
Detection Algorithm ◽  
Natural Environments ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Color Similarity ◽  
Feature Pyramid

The accurate detection of green citrus in natural environments is a key step in realizing the intelligent harvesting of citrus through robotics. At present, the visual detection algorithms for green citrus in natural environments still have poor accuracy and robustness due to the color similarity between fruits and backgrounds. This study proposed a multi-scale convolutional neural network (CNN) named YOLO BP to detect green citrus in natural environments. Firstly, the backbone network, CSPDarknet53, was trimmed to extract high-quality features and improve the real-time performance of the network. Then, by removing the redundant nodes of the Path Aggregation Network (PANet) and adding additional connections, a bi-directional feature pyramid network (Bi-PANet) was proposed to efficiently fuse the multilayer features. Finally, three groups of green citrus detection experiments were designed to evaluate the network performance. The results showed that the accuracy, recall, mean average precision (mAP), and detection speed of YOLO BP were 86, 91, and 91.55% and 18 frames per second (FPS), respectively, which were 2, 7, and 4.3% and 1 FPS higher than those of YOLO v4. The proposed detection algorithm had strong robustness and high accuracy in the complex orchard environment, which provides technical support for green fruit detection in natural environments.

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