A lightweight DRR-YOLOv11s model for power equipment failure and personal protective equipment detection in hydropower stations.

Ping Yu, Baoshu Zong, Xiaozhong Geng, Shuaiting Chen, Yong Li, Xiaoqing Xu

Abstract read

In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Ping YuSchool of Computer Technology and Engineering, Changchun Institute of Technology, Changchun, 130012, China.

Baoshu ZongSchool of Energy and Power Engineering, Changchun Institute of Technology, Changchun, 130012, China.

Xiaozhong GengSchool of Computer Technology and Engineering, Changchun Institute of Technology, Changchun, 130012, China. dq_gxz@ccit.edu.cn.

Shuaiting ChenSchool of Energy and Power Engineering, Changchun Institute of Technology, Changchun, 130012, China.

Yong LiSchool of Energy and Power Engineering, Changchun Institute of Technology, Changchun, 130012, China.

Xiaoqing XuSchool of Energy and Power Engineering, Changchun Institute of Technology, Changchun, 130012, China.

Funding

Supported by Jilin Provincial Science and Technology Department 20240404058ZP

6 · The paper itself

Abstract

The increasing demand for intelligent operation and maintenance in hydropower plants highlights the limitations of conventional inspection approaches in real-time performance and reliability, especially under edge-computing constraints. This paper presents DRR-YOLOv11s, a lightweight object detector for electrical equipment fault detection and worker safety monitoring. By optimizing the model structure for efficient inference, DRR-YOLOv11s reduces parameters by 37.58% and computational cost by 28.64% compared with the baseline, resulting in 5.88 M parameters and 15.2 GFLOPs. Experiments on the Electrical Equipment Failure dataset show a precision of 92.50%, recall of 89.42%, mAP@0.5 of 93.46%, and mAP@0.5:0.95 of 73.45%, while achieving 160.22 FPS. Cross-dataset evaluation on a Personal Protective Equipment dataset further indicates good generalization. Overall, DRR-YOLOv11s achieves a favorable trade-off between accuracy and efficiency, supporting practical deployment for real-time monitoring in hydropower plants.

Indexed as

Hydroelectric plantLightweight modelingPersonal protective equipmentPower equipment failure

Identifiers

PMID42031959

DOI10.1038/s41598-026-49094-x

PMCPMC13276164

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LicenceCC BY-NC-ND

Read underepoch 390

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.