[1]ZHAO Zhenbing,MA Teng,JING Qun,et al.Substation instrument appearance defect detection algorithm integrating causal knowledge[J].CAAI Transactions on Intelligent Systems,2026,21(4):988-1003.[doi:10.11992/tis.202508018]
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Substation instrument appearance defect detection algorithm integrating causal knowledge

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 4 Page number: 988-1003 Column: 学术论文—智能系统 Public date: 2026-07-05
Title:
Substation instrument appearance defect detection algorithm integrating causal knowledge
Author(s):
ZHAO Zhenbing1; 2; 3;  MA Teng1;  JING Qun1;  LI Jiabing1;  LI Haopeng1; 2;  ZHAO Wenqing3; 4
1. Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, China;
2. Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding 071003, China;
3. Engineering Research Center of Intelligent Computing for Complex Energy Systems of Ministry of Education, North China Electric Power University, Baoding 071003, China;
4. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China
Keywords:
target detection; substation instrument; appearance defect detection; attention mechanism; global-local feature extraction; cross-layer context feature pyramid network; causal knowledge association; loss function
CLC:
TP391
DOI:
10.11992/tis.202508018
Abstract:
To address the challenges of multi-scale feature extraction for appearance defects and class imbalance in substation meter defect detection, a substation meter appearance defect detection algorithm integrating causal knowledge is proposed. First, a global-to-local feature extraction block (GLFEB) is introduced into the backbone network to enhance the model’s ability to capture both global and local defect features. Then, a cross-layer context feature pyramid network (CCFPN) is incorporated into the neck to strengthen the model’s feature fusion capability. Finally, by deeply exploring the causal mechanisms underlying substation meter appearance defects, a causal knowledge association block (CKAB) is proposed. A loss function incorporating causal knowledge weights is designed to assist model training, thereby improving detection accuracy in complex substation environments. Experimental results demonstrate that, compared with the baseline model, the proposed method improves the mean average precision (mAP) by 4.1%, effectively enhancing the performance of substation meter appearance defect detection.
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