摘要
通过监测火灾烟雾可以有效地检测火灾的发生,现有火灾烟雾分割算法在小目标烟雾以及大目标烟雾边缘部分表现不理想,为快速而有效地监测烟雾,基于深度学习,提出一种改进的轻量化DeepLabV3+烟雾分割算法。该文通过替换DeepLabV3+算法的主干特征提取网络,在减少参数量的同时,提高了算法提取特征的能力和对烟雾的分割能力;同时在编码模块中添加卷积注意力模块(convolutional block attention module,CBAM),增加算法对小目标烟雾的关注度,以提升算法对烟雾在复杂背景下的分割能力,并有效缓解烟雾边缘的误分割现象。最后通过比对测试集的测试结果,改进的烟雾分割算法相较于原DeepLabV3+算法,烟雾交并比(smoke intersection over union,sIoU)、平均交并比(mean intersection over union,mIoU)和平均像素精确度(mean pixel accuracy,mPA)分别提高了6.46%、4.28%和1.72%,且改进算法的权重大小仅为原算法权重大小的10.76%。实验结果表明,改进的烟雾分割算法具有分割精度高、训练时间短且模型小的优点,更符合实际中的烟雾监测任务。
The fires can be effectively detected by monitoring fire smoke.However,existing fire smoke segmentation algorithms have not performed well on the small smoke and edges of large smoke.This article proposed an improved lightweight DeepLabV3+smoke segmentation algorithm based on deep learning,which effectively detects smoke.The feature extraction network of the DeeplabV3+algorithm was replaced,which reduced the number of parameters.This improvement enhanced the algorithm′s ability to extract smoke features and segment smoke.The convolutional block attention module(CBAM)was added to the encoder module to enhance the algorithm′s perception of small smoke.This improvement enhanced the algorithm′s segmentation capability for smoke in complex backgrounds,and effectively alleviate smoke mis-segmentation.Experimental results on the test set show a noticeable gain up to 6.46%in smoke intersection over union(sIoU),4.28%in mean intersection over union(mIoU),and 1.72%in mean pixel accuracy(mPA),respectively.Moreover,the improved algorithm′s weight size is only 10.76%of the original algorithm′s weight size.The experimental results show that the improved smoke segmentation algorithm,which has higher smoke segmentation accuracy,shorter training time,and a smaller model size compared to the original DeepLabV3+algorithm.The improved smoke segmentation algorithm is more suitable for real-time smoke monitoring tasks.
作者
陈鑫
侯青山
付艳
张吉康
CHEN Xin;HOU Qingshan;FU Yan;ZHANG Jikang(School of Electronics and Information,Xi’an Polytechnic University,Xi’an 710048,China;School of Automatic,Northwestern Polytechnical University,Xi’an 710129,China;Shaanxi Modern Architectural Design Research Institute,Xi’an 710048,China)
出处
《西安工程大学学报》
CAS
2023年第4期118-126,共9页
Journal of Xi’an Polytechnic University
基金
中国博士后科学基金面上项目(2020M683562)
陕西省科技厅自然科学基金面上项目(2022JM-331)
陕西省科技厅重点研发计划项目(2023-YBGY-142)。
