A Study on the Verification Scheme for Electrical Circuit Analysis of Fire Hazard Analysis in Nuclear Power Plant

Yim, Hyuntae; Oh, Seung-Jun; Kim, Weekyong

doi:10.14346/jkosos.2015.30.3.114

Cited by 2 publications

(1 citation statement)

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“…In the process of rapid economic development in today's cities, people's lives and property as well as the normal operation of enterprises are often threatened by fire. Fire has the characteristics of suddenness and great harm [1,2], and there is a clearly realistic demand for fire detection and early warning. At present, there are three fire detection and alarm methods: sensor based, image processing, and depth learning.…”

Section: Introductionmentioning

confidence: 99%

Research on Application of the Feature Transfer Method Based on Fast R-CNN in Smoke Image Recognition

Cheng

2021

Advances in Multimedia

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Most of the existing smoke detection methods are based on manual operation, which is difficult to meet the needs of fire monitoring. To further improve the accuracy of smoke detection, an automatic feature extraction and classification method based on fast regional convolution neural network (fast R–CNN) was introduced in the study. This method uses a selective search algorithm to obtain the candidate images of the sample images. The preselected area coordinates and the sample image of visual task are used as network learning. During the training process, we use the feature migration method to avoid the lack of smoke data or limited data sources. Finally, a target detection model is obtained, which is strongly related to a specified visual task, and it has well-trained weight parameters. Experimental results show that this method not only improves the detection accuracy but also effectively reduces the false alarm rate. It can not only meet the real time and accuracy of fire detection but also realize effective fire detection. Compared with similar fire detection algorithms, the improved algorithm proposed in this paper has better robustness to fire detection and has better performance in accuracy and speed.

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Section: Introductionmentioning

confidence: 99%

Research on Application of the Feature Transfer Method Based on Fast R-CNN in Smoke Image Recognition

Cheng

2021

Advances in Multimedia

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Detection of Smoke from Straw Burning Using Sentinel-2 Satellite Data and an Improved YOLOv5s Algorithm

Liu

et al. 2023

Remote Sensing

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The burning of straw is a very destructive process that threatens people’s livelihoods and property and causes irreparable environmental damage. It is therefore essential to detect and control the burning of straw. In this study, we analyzed Sentinel-2 data to select the best separation bands based on the response characteristics of clouds, smoke, water bodies, and background (vegetation and bare soil) to the different bands. The selected bands were added to the red, green, and blue bands (RGB) as training sample data. The band that featured the highest detection accuracy, RGB_Band6, was finally selected, having an accuracy of 82.90%. The existing object detection model cannot directly handle multi-band images. This study modified the input layer structure based on the YOLOv5s model to build an object detection network suitable for multi-band remote sensing images. The Squeeze-and-Excitation (SE) network attention mechanism was introduced based on the YOLOv5s model so that the delicate features of smoke were enhanced, and the Convolution + Batch normalization + Leaky ReLU (CBL) module was replaced with the Convolution + Batch normalization + Mish (CBM) module. The accuracy of the model was improved to 75.63%, which was 1.81% better than before. We also discussed the effect of spatial resolution on model detection and where accuracies of 84.18%, 73.13%, and 45.05% for images of 60-, 20-, and 10-m resolution, respectively, were realized. The experimental results demonstrated that the accuracy of the model only sometimes improved with increasing spatial resolution. This study provides a technical reference for the monitoring of straw burning, which is vital for both the control of straw burning and ways to improve ambient air quality.

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A Study on the Verification Scheme for Electrical Circuit Analysis of Fire Hazard Analysis in Nuclear Power Plant

Cited by 2 publications

References 1 publication

Research on Application of the Feature Transfer Method Based on Fast R-CNN in Smoke Image Recognition

Research on Application of the Feature Transfer Method Based on Fast R-CNN in Smoke Image Recognition

Detection of Smoke from Straw Burning Using Sentinel-2 Satellite Data and an Improved YOLOv5s Algorithm

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