Specificity autocorrelation integration network for surface defect detection of no-service rail

Yunhui Yan, Xiujian Jia, Kechen Song*, Wenqi Cui, Ying Zhao, Chuang Liu, Jingbo Guo*

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

8 引用 (Scopus)

摘要

Rails are critical to the safe transportation of railway system, and their surface quality is a vital aspect to consider. Existing defect detection methods struggle to identify irregular defect boundaries and distinguish the similarity of foreground and background. To address these issues, depth images are introduced to detect rail defects. However, the existing RGB-D SOD methods usually fuse two modalities without considering modality-specific characteristics. In this paper, we propose a specificity autocorrelation integration network (SAINet) for surface defect detection of rails. SAINet enhances defect detection performance by exploring autocorrelation features of a single modality and the specificity of each modality. Two decoders are carefully designed to capture the specific characteristics of each modality. Moreover, we propose a cross-modal autocorrelation attention fusion (CAAF) to effectively utilize the two modalities of information. It obtains autocorrelation features of RGB images through dilated convolution and attention modules, introducing depth features to locate defects more accurately. We design a multi-modal feature integration block (MFIB) to supplement the cross-modal features with modality-specific features output by each individual decoder, in order to boost SOD performance. SAINet's performance is verified on the NEU RSDDS-AUG dataset. Our network achieves the best results compared to the 25 state-of-the-art methods. We also validate SAINet's generalization performance on six other benchmark datasets, where the experiments show it competes well on these datasets. The code and results are available at https://github.com/VDT-2048/SAINet.

源语言英语
文章编号107862
期刊Optics and Lasers in Engineering
172
DOI
出版状态已出版 - 1月 2024
已对外发布

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引用此

Yan, Y., Jia, X., Song, K., Cui, W., Zhao, Y., Liu, C., & Guo, J. (2024). Specificity autocorrelation integration network for surface defect detection of no-service rail. Optics and Lasers in Engineering, 172, 文章 107862. https://doi.org/10.1016/j.optlaseng.2023.107862