基于改进YOLOv5的林火检测技术研究

消防科学与技术 ›› 2023, Vol. 42 ›› Issue (8): 1117-1120.

基于改进YOLOv5的林火检测技术研究

王乃宇1, 王琢1,2, 张子超1, 吴金霆1

（1. 东北林业大学机电工程学院，黑龙江哈尔滨 150040；2. 东北林业大学林业人工智能研究院，黑龙江哈尔滨 150040）

出版日期:2023-08-15 发布日期:2023-08-15
作者简介:作者简介：王乃宇（1998- ），男，河南新乡人，东北林业大学机电工程学院硕士研究生，主要从事图像处理、林火检测研究，黑龙江省哈尔滨市香坊区和兴路26号，150040。
基金资助:
基金项目：中央高校基本科研业务费专项资金资助项目（2572019CP21）；黑龙江省自然科学基金项目（TD2020C001）

Reserch on forest fire detection base on improved YOLOv5

Wang Naiyu1, Wang Zhuo1,2, Zhang Zichao1, Wu Jinting1

(1. School of Mechanical and Electrical Engineering,Northeast Forestry University,Heilongjiang Harbin 150040,China;2. Research Institute of Forestry Artificial Intelligence,Northeast Forestry University,Heilongjiang Harbin 150040,China)

Online:2023-08-15 Published:2023-08-15

摘要/Abstract

摘要： 为提高林火检测的准确率和检测速度，增强林火检测模型的实用性，提出了一种改进YOLOv5的林火检测算法。该算法将改进后的MobileViT作为YOLOv5的骨干网络，使网络能够更有效地提取林火特征信息，同时为进一步降低模型复杂度，采用深度可分离卷积替代模型中的普通卷积，在训练阶段引入了Mosaic数据增强的方法，以提高模型的泛化性。结果表明：改进后模型的林火检测精确率提高了2.25%，mAP提高了4.48%，检测速度提高了4帧/s，检测准确率和检测速度均取得了良好的效果。改进后模型能够很好地检测林火，提高了林火检测模型的实用性。

关键词: 林火检测, YOLOv5, MobileViT, 深度可分离卷积

Abstract: Abstract: In order to improve the accuracy and speed of forest fire detection and enhance the practicability of forest fire detection model, an improved YOLOv5 forest fire detection algorithm was proposed. In this algorithm, the improved MobileViT was used as the backbone network of YOLOv5, so that the network could extract forest fire feature information more effectively. Meanwhile, in order to further reduce the complexity of the model, depthwise separable convolution was used to replace the common convolution in the model, and Mosaic data enhancement method was introduced in the training stage to improve the generalization of the model. The results show that the forest fire detection accuracy of the improved model is increased by 2.25%, mAP by 4.48%, and detection speed by 4 frames/s. Both the detection accuracy and detection speed have achieved good results. The improved model can detect forest fire well and improve the practicability of forest fire detection model. The algorithm in this paper is more competent for the task of forest fire detection.

Key words: forest fire detection, YOLOv5, MobileViT, depthwise separable convolution

王乃宇, 王琢, 张子超, 吴金霆. 基于改进YOLOv5的林火检测技术研究[J]. 消防科学与技术, 2023, 42(8): 1117-1120.

Wang Naiyu, Wang Zhuo, Zhang Zichao, Wu Jinting. Reserch on forest fire detection base on improved YOLOv5[J]. Fire Science and Technology, 2023, 42(8): 1117-1120.