Work

Research on transformer fire flame detection and localization technology based on Binocular Vision

Year: 2024

Type: conference-paper

Abstract: Transformer fire is hidden, three-dimensional, and multi-scale, which is difficult to detect. In this paper, combined with the efficiency of deep learning in flame detection and the positioning advantages of binocular stereo vision, the transformer fire flame detection and positioning algorithm is proposed, and verified by experiments. The experimental platform of transformer oil pool fire was built, 122 sets of positioning experiments were carried out with 6 kinds of camera baselines, and the positioning rules and ranging range of the binocular system were determined. Under the condition that the camera baseline is 50 cm, the transformer fire flame detection and positioning experiment is carried out. The results show that the positioning error at 3 m to 8 m can be kept within 0.2 m, and the positioning error at 6 m is 1.3%.

Source: International Conference on Communication and Information Processing (ICCIP)

DOI: 10.1145/3708657.3708740

Authors: Xuan Chen, Haiming Yang, Bo Song, Peng Chen, Zhihao Xi

Institutions: State Grid Jiangsu Electric Power (China), China University of Mining and Technology - Beijing

Language: English

Topic: Fire Detection and Safety Systems

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

Funder: State Grid Jiangsu Electric Power

Award: G2817746879