A Lightweight Spatial and Temporal Multi-Feature Fusion Network for Defect Detection

Hu, Bozhen, Gao, Bin, Woo, Wai Lok, Ruan, Lingfeng, Jin, Jikun, Yang, Yang and Yu, Yongjie (2020) A Lightweight Spatial and Temporal Multi-Feature Fusion Network for Defect Detection. IEEE Transactions on Image Processing, 30. pp. 472-486. ISSN 1057-7149

ALightweightSpatialandTemporalMulti-featureFusionNetworkforDefectDetection.pdf - Accepted Version

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Official URL: https://doi.org/10.1109/TIP.2020.3036770


This article proposes a hybrid multi-dimensional features fusion structure of spatial and temporal segmentation model for automated thermography defects detection. In addition, the newly designed attention block encourages local interaction among the neighboring pixels to recalibrate the feature maps adaptively. A Sequence-PCA layer is embedded in the network to provide enhanced semantic information. The final model results in a lightweight structure with smaller number of parameters and yet yields uncompromising performance after model compression. The proposed model allows better capture of the semantic information to improve the detection rate in an end-to-end procedure. Compared with current state-of-the-art deep semantic segmentation algorithms, the proposed model presents more accurate and robust results. In addition, the proposed attention module has led to improved performance on two classification tasks compared with other prevalent attention blocks. In order to verify the effectiveness and robustness of the proposed model, experimental studies have been carried out for defects detection on four different datasets. The demo code of the proposed method can be linked soon: http://faculty.uestc.edu.cn/gaobin/zh_CN/lwcg/153392/list/index.htm

Item Type: Article
Additional Information: Funding Information: This work was supported in part by the Defense Industrial Technology Development Program under Grant JSZL2019205C003; in part by the National Natural Science Foundation of China under Grant 61971093, Grant 61527803, and Grant 61960206010; in part by the Science and Technology Department of Sichuan, China, under Grant 2019YJ0208, Grant 2018JY0655, and Grant 2018GZ0047; and in part by the Fundamental Research Funds for the Central Universities under Grant ZYGX2019J067.
Uncontrolled Keywords: attention, defect detection, Image segmentation, model compression, sequence-PCA
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 19 Apr 2022 09:44
Last Modified: 19 Apr 2022 09:45
URI: http://nrl.northumbria.ac.uk/id/eprint/48908

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