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Focus on local: transmission line defect detection via feature refinement

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Abstract

Different from the object detection which has made great progress in natural imagery, transmission line images acquired by UAVs have their own challenges in detecting defects in critical parts, such as object scale variation and small defect targets. In this paper, we construct an effective architecture, called FOLO, to improve the accuracy of defect detection in critical parts of transmission lines. To capture the critical part defect object features, a local contextual feature pyramid network (LCFPN) is proposed to refine the local contextual information and perform multi-scale learning. In LCFPN, we introduce a channel feature refinement block (CFRB) and multiple spatial feature refinement block (SFRBs) to further improve the ability of the network to focus on local features. Besides, a local adaptive feature network (LAFN) is designed, which makes it possible to locate adaptive components with defects in critical areas of different shapes. Since existing transmission line datasets have a single category, we create a new defect detection dataset containing insulators, anti-vibration hammers and bird nests, named IVB. Experimental results on IVB show that the proposed FOLO yields promising performance against other approaches.

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Author notes

  1. Yufeng Li and Longgang Dai have contributed equally to this work.

Authors and Affiliations

  1. College of Electronic Information Engineering, Shenyang Aerospace University, Shenyang, 110136, China

    Yufeng Li, Longgang Dai, Caihua Kong & Xiang Chen

  2. College of Electrical and Information Engineering, Changchun Institute Of Technology, Changchun, 130012, China

    Hongxia Ni

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  1. Yufeng Li
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  2. Longgang Dai
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  3. Hongxia Ni
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  4. Caihua Kong
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  5. Xiang Chen
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Correspondence to Longgang Dai.

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This work was supported by the Science and Technology Development Project of Jilin Province (Grant 20190302036GX).

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Li, Y., Dai, L., Ni, H. et al. Focus on local: transmission line defect detection via feature refinement. SIViP 17, 31–37 (2023). https://doi.org/10.1007/s11760-022-02200-8

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  • DOI: https://doi.org/10.1007/s11760-022-02200-8

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