Performance Evaluation of YOLO-YCbCr Based Smoke Segmentation Method

• 발행기관: 한국화재소방학회
• 저자명: Joohyung Roh Sehong Min Minsuk Kong
• 간행물 정보: 『International Journal of Fire Science and Engineering』Vol. 39, No. 2, 113~123쪽, 전체 11쪽
• 주제분류: 공학 > 공학일반
• 파일형태: PDF
• 발행일자: 2025.06.30

국문 초록

YOLO-YCbCr based smoke segmentation method for fire detection was proposed in this study to enhance smoke segmentation performance with reduction in computational cost. The proposed method consists of the deep learning object detection model, You Only Look Once (YOLO), and the color space-based segmentation model, rule-based YCbCr. YOLO is used to detect smoke objects in the input fire images through bounding boxes. The images in the bounding boxes are characterized based on YCbCr color space and the proposed YCbCr rules derived by considering smoke image characteristics segment smoke region. In order to evaluate the smoke segmentation performance, six different fire incident video data were used. The mean intersection over union (IoU) value of the proposed method was improved by approximately 31.3% and 24.5% respectively when compared to the reference models: YOLO-RGB model and YOLO-CIELAB model. It was found that YOLO significantly reduced the erroneous smoke segmentation and YCbCr rules derived from the smoke color features were effective in the smoke segmentation.

목차

1. Introduction

2. Material and Methods

3. Results

4. Conclusions

References

키워드

Smoke segmentation Fire detection YCbCr YOLO Performance evaluation