基于改进RT-DETR的火灾探测模型
Fire Detection Model Based on Improved RT-DETR
吴晓宁 1司占军1
作者信息
- 1. 天津科技大学 人工智能学院,天津 300457
- 折叠
摘要
火灾探测对人们的生命安全有很大的影响.Fire Detection-DETR(FD-DETR)是一种基于RT-DETR的火灾探测模型,其用于复杂火灾场景的早期火灾识别.本研究采用Adown子采样模块对原卷积模块进行改进,提高了检测精度,减少了参数值的个数.在骨干网络上使用LSKA注意力模块进一步提高了检测精度.实验结果表明,与原始RT-DETR模型相比,FD-DETR火焰检测的精确度和mAP分别提高了0.8%和0.1%.这证明本研究提出的改进方法有效地提高了网络的特征提取和特征融合能力,并且在复杂场景火灾检测任务中,改进RT-DETR算法的性能优于原始RT-DETR算法.
Abstract
Fire detection has a great impact on people's life safety.Fire Detection-DETR(FD-DETR)is a fire detection model based on RT-DETR for early fire identification in complex fire scenes.In this study,Adown sub-sampling module was selected to improve the original convolution module,which improved the detection accuracy and reduced the number of parameter values.Using LSKA attention module on the backbone network further improved the detection accuracy.The experimental results showed that compared with the original RT-DETR model,the precision and mAP of FD-DETR flame detection are increased by 0.8%and 0.1%,respectively,which proves that the improved method proposed in this study effectively improves the feature extraction and feature fusion capabilities of the network.In the complex scene fire detection task,the performance of the improved RT-DETR algorithm is better than the original RT-DETR algorithm.
关键词
火灾探测/RT-DETR/注意机制Key words
Fire detection/RT-DETR/Attention mechanism引用本文复制引用
出版年
2024
NETL
NSTL
万方数据