中圖分類(lèi)號(hào)： TP391.4 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.211974 中文引用格式： 李寧，張彥輝,，尚英強(qiáng)，等. 基于改進(jìn)DeepLabv3+網(wǎng)絡(luò)的風(fēng)機(jī)葉片分割算法研究[J].電子技術(shù)應(yīng)用,，2022,，48(9)：108-113，118. 英文引用格式： Li Ning,，Zhang Yanhui,，Shang Yingqiang，et al. Research on fan blade segmentation algorithm based on improved DeepLabv3+ network[J]. Application of Electronic Technique,，2022,，48(9)：108-113，118.
Research on fan blade segmentation algorithm based on improved DeepLabv3+ network
Li Ning,，Zhang Yanhui,，Shang Yingqiang，Zhou Ge,，Gao Jinqiu
Cable Branch of Beijing Electric Power Company,，Beijing 100010，China
Abstract： In order to improve the segmentation quality of fan blade image, this paper proposes a fan blade segmentation algorithm based on improved DeepLabv3+ network. Due to the problems of background assistance and large difference in the proportion of blades collected by UAV, the algorithm proposed in this paper improves the ASPP module and decoder module based on the DeepLabv3+ network. DSAPP concatenates multiple hole convolutions, and transfers the output of each hole convolution layer to the subsequent hole convolution layer by using dense connection. Through a series of feature connections, DSAPP encodes intermediate features of different scales, and obtains a larger range of receptive fields. In the decoder stage, multi-layer feature fusion is added to recover the detail information and all levels of features lost in the down sampling process. Through the experiment of fan blade data set, the MIoU value reaches 0.991 3, PA value reaches 0.996 8. The experimental results show that the segmentation effect of the algorithm designed in this paper is better than that of DeepLabv3+network, and has better detail information.
Key words : fan blade,；image segmentation,；DeepLabv3+；DASPP
