[1]ZHAO Zhenbing,XI Yue,FENG Shuo,et al.Corrosion detection method for complex scenario substation equipment[J].CAAI Transactions on Intelligent Systems,2025,20(3):679-688.[doi:10.11992/tis.202403044]
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Corrosion detection method for complex scenario substation equipment

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 3 Page number: 679-688 Column: 学术论文—智能系统 Public date: 2025-05-05
Title:
Corrosion detection method for complex scenario substation equipment
Author(s):
ZHAO Zhenbing1; 2; 3;  XI Yue1;  FENG Shuo1;  ZHAO Wenqing2; 4;  ZHAI Yongjie4;  LI Bing4
1. Department of Electronic and Communication Engineering, North China Electric Power University, Baoding 071003, China;
2. Engineering Research Center of Intelligent Computing for Complex Energy Systems of Ministry of Education, North China Electric Power University, Baoding 071003, China;
3. Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding 071003, China;
4. School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China
Keywords:
substation equipment; irregular defect; corrosion detection; YOLOv8; attention mechanism; multi-scale feature; detection head; complex scenarios; power computer vision
CLC:
TP183
DOI:
10.11992/tis.202403044
Abstract:
To address the significant challenges posed by significant variations in corrosion morphology, varying scales, and low feature saliency in corrosion detection in substation equipment under complex scenarios, a corrosion detection method for complex scenario substation equipment is proposed. First, frequency channel attention networks are introduced, leveraging additional frequency components to complement the fine-grained features in deep networks, thus optimizing the extraction of corrosion-related features. Second, within the feature fusion network, the multi-scale module is employed to rebuild the C2f module, thereby allowing the network to better capture corrosion areas of different sizes. Finally, the additional detection head mitigates crucial corrosion-related information loss during feature fusion caused by downsampling, thereby enhancing corrosion detection accuracy. The relevant experimental results reveal a 5.1% improvement in the average detection accuracy (mAP50/%) compared with the results obtained by the original YOLOv8m model. Overall, this improved model outperforms other mainstream object detection models, thereby representing a novel approach for substation equipment corrosion detection.
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