基于高斯建模的电力场景明火检测

Flame detection in electric power scenarios based on Gaussian modeling

基于深度学习的电力场景明火识别研究尚存在两个问题:一是相比于通用物体的清晰边界,火焰的边界具有很强的模糊性;二是基于深度学习的方法需要大量有标签的数据进行训练,但是目前严重缺乏电力场景下大规模的明火检测数据。针对第一个问题,通过对传统YOLOv5检测器进行改进,基于高斯建模捕获边框的不确定性;针对第二个问题,提出一种基于迁移学习的两阶段训练方法,只需要少量电力场景明火图片即可实现高精度的电力场景明火检测器训练。为了验证所提方法的有效性,利用网络公开数据及自有数据进行对比实验,结果表明所提出的方法具有很强的适用性。

There are two problems in flame detection in electric power scenarios based on deep learning:1)In contrast to the clear boundaries of general objects,the flame boundary is much hazy;2)The deep learning-based methods require a large amount of labeled data for training;however,there is a severe shortage of large-scale flame detection data in electric power scenarios.In response to the first problem,this paper improves the traditional YOLOv5 detector to capture the uncertainty of the frame through Gaussian modeling.For the second question,this paper proposes a two-stage training method based on migration learning,which requires only a small number of flame data of the power scenarios for high-precision flame detector training.For verifying the effectiveness of the proposed method,accessible data on the internet and private data are used for contrast experiment.The results show that the proposed method is of great adaptability.