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Efficient and Refined Deep Convolutional Features Network for the Crack Segmentation of Solar Cell Electroluminescence Images

High-quality and fast crack segmentation in solar cell electroluminescence images with heterogeneously textured background disturbance is challenging. We propose an end-to-end Efficient and Refined Deep Convolutional Features Network (ERDCF-Net) for precise and efficient crack segmentation. Firstly, we design a lightweight, efficient deep convolutional features (EDCF) encoder by adopting multilevel deep convolutional features to generate a discriminative feature representation of cracks. Moreover, some complicated background disturbance is restrained by reusing rich crack and background features. Secondly, we present a refined side output (RS) decoder by automatically refining side outputs from the EDCF encoder. The experiments demonstrate that the proposed network excellently performs on the solar cell cracks (MIoU of 92.82%, F - measure of 93.58%, and 89 FPS) and open crack datasets (MIoU of 85.6%, F - measure of 84.1%, and 90 FPS).

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING; Vol.35 No.4 November 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 610-619

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

CONVOLUTIONAL NEURAL NETWORK
SOLAR CELLS
DEEP LEARNING
CRACK SEGMENTATION
DEEP CONVOLUTIONAL FEATURES
QUALITY CLASSIFICATION OF SOLAR CELLS

Info Detail Spesifik

DOI: 10.1109/TSM.2022.3197933

Pernyataan Tanggungjawab

Chuhan Wang, Haiyong Chen, Shenshen Zhao, Muhammad Rameez Ur Rahman

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