Flame detection in electric power scenarios based on Gaussian modeling

Abstract

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.

Keywords

• electric power scenario
• flame recognition
• deep learning
• object detection