Application of Improved YOLOv5 Algorithm in Parking Lot Fire Detection

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Application of Improved YOLOv5 Algorithm in Parking Lot Fire Detection

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[1]ZHANG Zhen,JIN Zhihua,CHEN Kexin.Application of Improved YOLOv5 Algorithm in Parking Lot Fire Detection[J].Journal of Zhengzhou University (Engineering Science),2023,44(04):16-21.[doi:10.13705/j.issn.1671-6833.2023.04.001]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 44卷 Number of periods: 2023 04 Page number: 16-21 Column: Public date: 2023-06-01

Title:: Application of Improved YOLOv5 Algorithm in Parking Lot Fire Detection

Author(s):: ZHANG Zhen¹; JIN Zhihua¹; CHEN Kexin²; School of Electrical and Information Engineering, Zhengzhou University, 450001, Zhengzhou, Henan, School of Computer and Artificial Intelligence at Zhengzhou University, 450001, Zhengzhou, Henan

Keywords:: underground parking; fire detection; YOLOv5; coordinate attention; CIoU loss function

CLC:: TP391

DOI:: 10.13705/j.issn.1671-6833.2023.04.001

Abstract:: Aiming at the problems that the traditional sensors are not timely in detecting the fire of underground parking lot and the object detection is not effective in detecting small flame targets, an improved YOLOv5 fire detection algorithm was proposed. In order to improve the detection effect of the detection algorithm on small flame targets, small target detection layer was added to YOLOv5s network backbone; In order to enhance the expression of flame features, an interval attention structure based on CA attention mechanism was proposed; In order to improve the positioning accuracy and reduce the rate of missed target detection, GIoU was replaced by CIoU. Three groups of ablation experiments and one group of contrast experiment were designed to verify the effectiveness of this improvement. The experimental results show that the mAP0. 5 and R of the algorithm on the user-defined dataset are 92% and 96. 9%, respectively. Compared with YOLOv5s model, the mAP0. 5 of the proposed algorithm on the customized flame data set is increased by 1. 8 percentage, and R is increased by 2. 0 percentage. The weight size of the proposed algorithm is only 16. 4 MB, and the frame rate can reach 113 frames per second. It has smaller model volume and faster detection speed, and can accurately detect small flame targets, effectively improving the fire prevention capability of the underground parking lot.

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Last Update: 2023-06-30