计算机科学
人工智能
鉴定(生物学)
计算机视觉
特征(语言学)
特征提取
图像(数学)
直线(几何图形)
遥感
模式识别(心理学)
地理
数学
植物
生物
语言学
哲学
几何学
作者
Wei He,Wenjun Chen,Yulin Wang,Yu Liu,Shenghong Wang
标识
DOI:10.1016/j.ijft.2023.100494
摘要
In recent years, many places have experienced frequent mountain fires, which have become one of the main disasters in the operation of power transmission lines. However, traditional manual inspection and video monitoring methods can only detect a small amount of mountain fires, and require a large amount of manpower and material resources. In this paper, image recognition technology was used to study the automatic identification algorithm of transmission line mountain fires, and image recognition technology was used to denoise the extracted images. After that, feature extraction was performed on the successfully denoised image, and the image was enhanced to improve the clarity of the image and prepare for improving the recognition accuracy of mountain fires. Through experiments, it can be found that using image recognition technology to identify mountain fires not only has high accuracy and recognition speed, but also has a lower error rate compared to using satellite meteorological data. The recognition accuracy of image recognition technology was above 95%, while the recognition accuracy of using satellite meteorological data was below 92%.
科研通智能强力驱动
Strongly Powered by AbleSci AI