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Open Access

ARTICLE

An RMD-YOLOv11 Approach for Typical Defect Detection of PV Modules

Tao Geng1, Shuaibing Li1,*, Yunyun Yun1, Yongqiang Kang1, Hongwei Li2, Junmin Zhu2
1 School of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China
2 School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China
* Corresponding Author: Shuaibing Li. Email: email
(This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems-2nd Edition)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.071644

Received 09 August 2025; Accepted 06 November 2025; Published online 27 November 2025

Abstract

In order to address the challenges posed by complex background interference, high miss-detection rates of micro-scale defects, and limited model deployment efficiency in photovoltaic (PV) module defect detection, this paper proposes an efficient detection framework based on an improved YOLOv11 architecture. First, a Re-parameterized Convolution (RepConv) module is integrated into the backbone to enhance the model’s sensitivity to fine-grained defects—such as micro-cracks and hot spots—while maintaining high inference efficiency. Second, a Multi-Scale Feature Fusion Convolutional Block Attention Mechanism (MSFF-CBAM) is designed to guide the network toward critical defect regions by jointly modeling channel-wise and spatial attention. This mechanism effectively strengthens the specificity and robustness of feature representations. Third, a lightweight Dynamic Sampling Module (DySample) is employed to replace conventional upsampling operations, thereby improving the localization accuracy of small-scale defect targets. Experimental evaluations conducted on the PVEL-AD dataset demonstrate that the proposed RMD-YOLOv11 model surpasses the baseline YOLOv11 in terms of mean Average Precision (mAP)@0.5, Precision, and Recall, achieving respective improvements of 4.70%, 1.51%, and 5.50%. The model also exhibits notable advantages in inference speed and model compactness. Further validation on the ELPV dataset confirms the model’s generalization capability, showing respective performance gains of 1.99%, 2.28%, and 1.45% across the same metrics. Overall, the enhanced model significantly improves the accuracy of micro-defect identification on PV module surfaces, effectively reducing both false negatives and false positives. This advancement provides a robust and reliable technical foundation for automated PV module defect detection.

Keywords

Photovoltaic (PV) modules; YOLOv11; re-parameterization convolution; attention mechanism; dynamic upsampling

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