计算机工程与应用 ›› 2020, Vol. 56 ›› Issue (21): 194-198.DOI: 10.3778/j.issn.1002-8331.2006-0194

• 图形图像处理 • 上一篇    下一篇

基于改进Mask R-CNN的火焰图像识别算法

喻丽春,刘金清   

  1. 1.福州外语外贸学院 理工学院,福州 350202
    2.福建师范大学 光电与信息工程学院,福州 350007
  • 出版日期:2020-11-01 发布日期:2020-11-03

Fire Image Recognition Algorithm Based on Improved Mask R-CNN

YU Lichun, LIU Jinqing   

  1. 1.School of Technology, Fuzhou University of International Studies and Trade, Fuzhou 350202, China
    2.College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China
  • Online:2020-11-01 Published:2020-11-03

摘要:

传统火焰检测算法依赖于人工特征工程,具有主观性和盲目性,存在泛化能力差,检测准确率不高等问题。提出一种基于改进Mask R-CNN的火焰图像识别算法,算法在Mask R-CNN的基础上,在特征金字塔引入一条自下向上的特征融合,同时改进了损失函数,使边框定位更准确。在自建的测试数据集上实验表明,改进后算法准确率相对于原先算法识别定位精度更高,检测准确率提升超过5%。

关键词: 火灾检测, 特征工程, 残差网络, 特征金字塔网络

Abstract:

The traditional fire detection algorithm relies on artificial feature engineering, which is subjective and blind. It has the problems of poor generalization ability and low detection accuracy. In this paper, a fire image detection algorithm based on improved Mask R-CNN is proposed. On the basis of Mask R-CNN, the algorithm introduces a bottom-up feature fusion in the feature pyramid, and improves the loss function to make the frame location more accurate. Experiments on the self-built test dataset show that the accuracy of the improved algorithm is higher than that of the original algorithm, and the detection accuracy is increased by more than 5%.

Key words: fire detection, feature engineering, residual network, Feature Pyramid Network(FPN)