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Volume 14
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10.3390/electronics14122383
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Article

Inspection Cover Damage Warning System Using Deep Learning Based on Data Fusion and Channel Attention

by
Kaiyu Zhang
1,
Baohua Wang
1,*,
Hongyan Chen
2,
Huaijun Peng
2,
Lei Xue
3,
Baojiang Han
3,
Zhili Tang
3 and
Yuzhang Liu
3,4
1
School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
2
BII Transit System (Beijing) Co., Ltd., Beijing 100029, China
3
Beijing Information Infrastructure Construction Co., Ltd., Beijing 100068, China
4
Beijing Infrastructure Investment Co., Ltd., Beijing 100101, China
*
Author to whom correspondence should be addressed.
Electronics 2025, 14(12), 2383; https://doi.org/10.3390/electronics14122383
Submission received: 12 May 2025 / Revised: 7 June 2025 / Accepted: 9 June 2025 / Published: 11 June 2025
(This article belongs to the Section Computer Science & Engineering)
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Abstract

This paper explores the application of artificial intelligence in urban energy infrastructure construction and enhances the operation and maintenance safety of infrastructure through edge computing and advanced sensors. At present, urban manhole covers cover a large number of roads, but there is a lack of effective real-time monitoring methods. In order to effectively solve these problems, this study proposes a domain adaptive network algorithm (EDDNet) based on data fusion. By optimizing the loss function, the attention mechanism is used to make the model pay more attention to the deep features related to the abnormal state of the inspection cover. The algorithm solves the problem of broadband vibration analysis and reduces the misclassification rate in various behavioral scenarios, including pedestrian traffic, slow-moving vehicles, and intentional surface collisions. A data acquisition sensor network is established, and a six-degree-of-freedom coupled vibration model and a structural vibration model of the inspection cover are established. The vibration peak under high load conditions is modeled and simulated using impact load data, and a fitting curve is generated to achieve deep optimization of the model and enhance robustness. The experimental results show that the classification accuracy of the network reaches 95.23%, which is at least 10.2% higher than the baseline model.
Keywords: attention mechanism; lightweight network architecture; vibration analysis; data imbalance; domain adaptation; edge computing attention mechanism; lightweight network architecture; vibration analysis; data imbalance; domain adaptation; edge computing

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MDPI and ACS Style

Zhang, K.; Wang, B.; Chen, H.; Peng, H.; Xue, L.; Han, B.; Tang, Z.; Liu, Y. Inspection Cover Damage Warning System Using Deep Learning Based on Data Fusion and Channel Attention. Electronics 2025, 14, 2383. https://doi.org/10.3390/electronics14122383

AMA Style

Zhang K, Wang B, Chen H, Peng H, Xue L, Han B, Tang Z, Liu Y. Inspection Cover Damage Warning System Using Deep Learning Based on Data Fusion and Channel Attention. Electronics. 2025; 14(12):2383. https://doi.org/10.3390/electronics14122383

Chicago/Turabian Style

Zhang, Kaiyu, Baohua Wang, Hongyan Chen, Huaijun Peng, Lei Xue, Baojiang Han, Zhili Tang, and Yuzhang Liu. 2025. "Inspection Cover Damage Warning System Using Deep Learning Based on Data Fusion and Channel Attention" Electronics 14, no. 12: 2383. https://doi.org/10.3390/electronics14122383

APA Style

Zhang, K., Wang, B., Chen, H., Peng, H., Xue, L., Han, B., Tang, Z., & Liu, Y. (2025). Inspection Cover Damage Warning System Using Deep Learning Based on Data Fusion and Channel Attention. Electronics, 14(12), 2383. https://doi.org/10.3390/electronics14122383

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