文献类型：期刊文献

英文题名：Fast flame recognition algorithm base on segmentation network

作者：Niu, Chunyu[1]; Guo, Hui[1]; Wang, Yong[2]

机构：[1] School of Mechanical and Power Engineering, East China University of Science and Technology, Shanghai, 200237, China; [2] School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao, 266033, China

年份：2023

起止页码：458

外文期刊名：Proceedings - 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2023

收录：EI(收录号：20232114124655)

语种：英文

外文关键词：Convolution - Deep learning - Information management

摘要：To solve the low recognition rate of the network to flame and keep the accuracy, we propose an Instance segmentation model for recognizing and locating flames more accurate This network is improved based on the deep learning model Mask R-CNN, it introduces four key components:(1) After analyzing the effects of space and channel attention, we used an efficient convolution channel attention. (2) By comparing the convolution kernel size, an optimized dilated convolution is added to the network, (3) To eliminate redundancy, reducing the depth of the backbone while guaranteeing the accuracy of the network. (4) Finally, Adding a flame extraction algorithm behind the head. Compared with Mask R-CNN, the model size is reduced by 16.3MB, and the recognition accuracy of flame is improved by 1.7%, The comparison shows that the network can also greatly improve the recognition effect of small flames. ? 2023 IEEE.