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A Lightweight Multiscale Feature Fusion Network for Solar Cell Defect Detection
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作者 Xiaoyun Chen Lanyao Zhang +3 位作者 Xiaoling Chen Yigang Cen Linna Zhang Fugui Zhang 《Computers, Materials & Continua》 SCIE EI 2025年第1期521-542,共22页
Solar cell defect detection is crucial for quality inspection in photovoltaic power generation modules.In the production process,defect samples occur infrequently and exhibit random shapes and sizes,which makes it cha... Solar cell defect detection is crucial for quality inspection in photovoltaic power generation modules.In the production process,defect samples occur infrequently and exhibit random shapes and sizes,which makes it challenging to collect defective samples.Additionally,the complex surface background of polysilicon cell wafers complicates the accurate identification and localization of defective regions.This paper proposes a novel Lightweight Multiscale Feature Fusion network(LMFF)to address these challenges.The network comprises a feature extraction network,a multi-scale feature fusion module(MFF),and a segmentation network.Specifically,a feature extraction network is proposed to obtain multi-scale feature outputs,and a multi-scale feature fusion module(MFF)is used to fuse multi-scale feature information effectively.In order to capture finer-grained multi-scale information from the fusion features,we propose a multi-scale attention module(MSA)in the segmentation network to enhance the network’s ability for small target detection.Moreover,depthwise separable convolutions are introduced to construct depthwise separable residual blocks(DSR)to reduce the model’s parameter number.Finally,to validate the proposed method’s defect segmentation and localization performance,we constructed three solar cell defect detection datasets:SolarCells,SolarCells-S,and PVEL-S.SolarCells and SolarCells-S are monocrystalline silicon datasets,and PVEL-S is a polycrystalline silicon dataset.Experimental results show that the IOU of our method on these three datasets can reach 68.5%,51.0%,and 92.7%,respectively,and the F1-Score can reach 81.3%,67.5%,and 96.2%,respectively,which surpasses other commonly usedmethods and verifies the effectiveness of our LMFF network. 展开更多
关键词 Defect segmentation multi-scale feature fusion multi-scale attention depthwise separable residual block
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Preparation of porous polyimide microspheres by thermal degradation of block copolymers 被引量:3
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作者 Li Wang Jianjun Lu +2 位作者 Miaoqing Liu Li Lin Jingjing Li 《Particuology》 SCIE EI CAS CSCD 2014年第3期63-70,共8页
A new preparation method has been developed for thermally stable porous polyimide microspheres. Porous polyimide microspheres were prepared using trib]ock copolymers that consisted of a thermally stable polyimide deri... A new preparation method has been developed for thermally stable porous polyimide microspheres. Porous polyimide microspheres were prepared using trib]ock copolymers that consisted of a thermally stable polyimide derived from pyromellitic dianhydride/4,4'-oxydianiline as the continuous phase and a thermally labile polyether as the dispersed phase. Spheres of copolymers were generated in a nonaqueous emulsion and then gradually heated to complete the imidization to form a microphase-separated structure. Subsequently, thermal treatment at a slightly reduced pressure removed the labile blocks and produced pores. Under suitable decomposition conditions, the pore size of the porous polyimide was in the range of 200-400nm. 展开更多
关键词 block copolymer sphere Microphase separation Polyimide Porous microsphere Porous polymer
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