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

Bozhen Hu, Bin Gao*, Wai Lok Woo, Lingfeng Ruan, Jikun Jin, Yang Yang, Yongjie Yu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)
3 Downloads (Pure)


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

Original languageEnglish
Article number9259247
Pages (from-to)472-486
Number of pages15
JournalIEEE Transactions on Image Processing
Early online date16 Nov 2020
Publication statusPublished - 23 Nov 2020


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