Deep Learning-Based Forest Fire Smoke Detection

doi:10.13466/j.cnki.lyzygl.2023.04.018

Abstract

Abstract:

In order to detect forest fires in the first time and avoid serious consequences caused by forest fires,a detection model YOLO-SCW with forest fire smoke as the main target is proposed,and the SPD-Conv layer is introduced based on YOLOv7 to reduce the problem of missing features of small targets in the feature extraction process.Then,the Coordinate Pay module is added in the pooling part of the detection head pyramid,and the location information is encoded into the channel,which increases the attention of the modelto the target and reduces the interference of the background on the detection effect.Finally,the WIoU rectangular box loss function is used to improve the regression speed and accuracy of the prediction box.During the test,the improved YOLO-SCW increased by 9.1% compared with the mAP of the YOLOv7 model,and reduced the false detection and missed detection,which proved that YOLO-SCW has better feature extraction and generalization ability,and has excellent performance for forest fire smoke detection tasks.

Key words: YOLO-SCW, forest fire smoke detection, object detection, deep learning, loss function

CLC Number:

S762.2

ZHENG Yanrui, YANG Linjian, LI Shuguang, ZHANG Yongju. Deep Learning-Based Forest Fire Smoke Detection[J]. FOREST RESOURCES WANAGEMENT, 2023, (4): 150-160.

Figures/Tables 15

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序号	gamma	SPD	CA	WIoU	精度/%	召回率/%	mAP(IoU=0.5)
1	×	×	×	×	87.2	83.7	86.3
2	√	×	×	×	90.3	89.1	91.2
3	×	√	×	×	91.1	90.4	93.9
4	×	×	√	×	91.9	85.0	91.4
5	×	×	×	√	80.0	81.6	83.8
6	√	√	√	×	90.5	91.1	93.4
7	√	√	√	√	94.4	92.5	95.4