The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Makalah ini membentangkan rangkaian YOLOv3 yang dipertingkatkan, dinamakan MSFF-YOLOv3, untuk mengesan kecacatan permukaan berubah-ubah profil aluminium dengan tepat dalam amalan. Pertama, kami memperkenalkan skala ramalan yang lebih besar untuk menyediakan maklumat terperinci untuk pengesanan kecacatan kecil; kedua, kami mereka bentuk blok berpandukan perhatian yang cekap untuk mengekstrak lebih banyak ciri kecacatan dengan kurang overhed; ketiga, kami mereka bentuk piramid bawah ke atas dan mengintegrasikannya dengan rangkaian piramid ciri sedia ada untuk membina struktur menara berkembar untuk meningkatkan peredaran dan gabungan ciri-ciri lapisan yang berbeza. Di samping itu, kami menggunakan algoritma K-median untuk pengelompokan anchor untuk mempercepatkan penaakulan rangkaian. Keputusan eksperimen menunjukkan bahawa purata ketepatan purata rangkaian MSFF-YOLOv3 yang dicadangkan adalah lebih tinggi daripada semua rangkaian konvensional untuk pengesanan kecacatan permukaan profil aluminium. Selain itu, bilangan bingkai yang diproses sesaat untuk cadangan MSFF-YOLOv3 kami boleh memenuhi keperluan masa nyata.
Lianshan SUN
Shaanxi University of Science and Technology
Jingxue WEI
Shaanxi University of Science and Technology
Hanchao DU
Shaanxi University of Science and Technology
Yongbin ZHANG
Shaanxi University of Science and Technology
Lifeng HE
Aichi Prefectural University
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Salinan
Lianshan SUN, Jingxue WEI, Hanchao DU, Yongbin ZHANG, Lifeng HE, "MSFF: A Multi-Scale Feature Fusion Network for Surface Defect Detection of Aluminum Profiles" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 9, pp. 1652-1655, September 2022, doi: 10.1587/transinf.2021EDL8088.
Abstract: This paper presents an improved YOLOv3 network, named MSFF-YOLOv3, for precisely detecting variable surface defects of aluminum profiles in practice. First, we introduce a larger prediction scale to provide detailed information for small defect detection; second, we design an efficient attention-guided block to extract more features of defects with less overhead; third, we design a bottom-up pyramid and integrate it with the existing feature pyramid network to construct a twin-tower structure to improve the circulation and fusion of features of different layers. In addition, we employ the K-median algorithm for anchor clustering to speed up the network reasoning. Experimental results showed that the mean average precision of the proposed network MSFF-YOLOv3 is higher than all conventional networks for surface defect detection of aluminum profiles. Moreover, the number of frames processed per second for our proposed MSFF-YOLOv3 could meet real-time requirements.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDL8088/_p
Salinan
@ARTICLE{e105-d_9_1652,
author={Lianshan SUN, Jingxue WEI, Hanchao DU, Yongbin ZHANG, Lifeng HE, },
journal={IEICE TRANSACTIONS on Information},
title={MSFF: A Multi-Scale Feature Fusion Network for Surface Defect Detection of Aluminum Profiles},
year={2022},
volume={E105-D},
number={9},
pages={1652-1655},
abstract={This paper presents an improved YOLOv3 network, named MSFF-YOLOv3, for precisely detecting variable surface defects of aluminum profiles in practice. First, we introduce a larger prediction scale to provide detailed information for small defect detection; second, we design an efficient attention-guided block to extract more features of defects with less overhead; third, we design a bottom-up pyramid and integrate it with the existing feature pyramid network to construct a twin-tower structure to improve the circulation and fusion of features of different layers. In addition, we employ the K-median algorithm for anchor clustering to speed up the network reasoning. Experimental results showed that the mean average precision of the proposed network MSFF-YOLOv3 is higher than all conventional networks for surface defect detection of aluminum profiles. Moreover, the number of frames processed per second for our proposed MSFF-YOLOv3 could meet real-time requirements.},
keywords={},
doi={10.1587/transinf.2021EDL8088},
ISSN={1745-1361},
month={September},}
Salinan
TY - JOUR
TI - MSFF: A Multi-Scale Feature Fusion Network for Surface Defect Detection of Aluminum Profiles
T2 - IEICE TRANSACTIONS on Information
SP - 1652
EP - 1655
AU - Lianshan SUN
AU - Jingxue WEI
AU - Hanchao DU
AU - Yongbin ZHANG
AU - Lifeng HE
PY - 2022
DO - 10.1587/transinf.2021EDL8088
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2022
AB - This paper presents an improved YOLOv3 network, named MSFF-YOLOv3, for precisely detecting variable surface defects of aluminum profiles in practice. First, we introduce a larger prediction scale to provide detailed information for small defect detection; second, we design an efficient attention-guided block to extract more features of defects with less overhead; third, we design a bottom-up pyramid and integrate it with the existing feature pyramid network to construct a twin-tower structure to improve the circulation and fusion of features of different layers. In addition, we employ the K-median algorithm for anchor clustering to speed up the network reasoning. Experimental results showed that the mean average precision of the proposed network MSFF-YOLOv3 is higher than all conventional networks for surface defect detection of aluminum profiles. Moreover, the number of frames processed per second for our proposed MSFF-YOLOv3 could meet real-time requirements.
ER -