Real-time detection method for photovoltaic electroluminescence images targeting weak and multi-scale defects

Xiang Chen; Liming Sun; Haiyang Sun; Yelin Deng; Xiang Chen; Liming Sun; Haiyang Sun; Yelin Deng

doi:10.3934/era.2026298

Electronic Research Archive

2026, Volume 34, Issue 9: 6846-6876. doi: 10.3934/era.2026298

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Real-time detection method for photovoltaic electroluminescence images targeting weak and multi-scale defects

1.
School of Big Data, LiaoNing Finance and Trade College, 167 Xinghai North Street, Xingcheng 125105, China
2.
School of Rail Transportation, Soochow University, 8 Jixue Road, Suzhou 215137, China
3.
School of Mechanical Engineering, Nantong University, 9 Seyuan Road, Nantong 226019, China
4.
School of Computer Science and Engineering, Northeastern University, 195 Chuangxin Road, Shenyang 110169, China

Received: 18 June 2026 Revised: 08 July 2026 Accepted: 28 July 2026 Published: 12 August 2026

Defect detection in photovoltaic (PV) modules is crucial for long-term reliable operation of solar plants, reducing levelized cost of energy, and preventing safety incidents. Electroluminescence (EL) images suffer from low contrast, complex background noise, and frequent overlapping defects, hindering the balance between detection accuracy and real-time processing speed for current algorithms. To address this, a novel real-time object detector is proposed. First, a convolutional additive self-attention block (CAS-Block) reconstructs the backbone by replacing dot-product with additive similarity, enhancing the response sensitivity to weak defect features in low-contrast images while maintaining linear computational complexity. Second, an efficient dynami-scale intra-feature interaction (EDII) module based on frequency-domain learning employs adaptive spectral filtering to suppress environmental noise interference, such as illumination variations. Finally, a gated cross-scale feature fusion (GCFF) module built on gated linear unit (GLU) uses a dynamic gating mechanism to precisely disentangle feature confusion arising from overlapping defects. Experimental results show that the proposed model achieves a mean average precision (mAP)@0.5 of 93.2% and a detection speed of 104.2 frames per second (FPS), outperforming the state-of-the-art (SOTA) YOLOv12-L and EER-DETR. The method achieves an optimal accuracy–speed trade-off, offering an efficient, cost-effective solution for intelligent inspection of large-scale PV plants.
- photovoltaic cells,
- defect detection,
- electroluminescence images,
- deep learning,
- real-time object detection
Citation: Xiang Chen, Liming Sun, Haiyang Sun, Yelin Deng. Real-time detection method for photovoltaic electroluminescence images targeting weak and multi-scale defects[J]. Electronic Research Archive, 2026, 34(9): 6846-6876. doi: 10.3934/era.2026298

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Abstract

Defect detection in photovoltaic (PV) modules is crucial for long-term reliable operation of solar plants, reducing levelized cost of energy, and preventing safety incidents. Electroluminescence (EL) images suffer from low contrast, complex background noise, and frequent overlapping defects, hindering the balance between detection accuracy and real-time processing speed for current algorithms. To address this, a novel real-time object detector is proposed. First, a convolutional additive self-attention block (CAS-Block) reconstructs the backbone by replacing dot-product with additive similarity, enhancing the response sensitivity to weak defect features in low-contrast images while maintaining linear computational complexity. Second, an efficient dynami-scale intra-feature interaction (EDII) module based on frequency-domain learning employs adaptive spectral filtering to suppress environmental noise interference, such as illumination variations. Finally, a gated cross-scale feature fusion (GCFF) module built on gated linear unit (GLU) uses a dynamic gating mechanism to precisely disentangle feature confusion arising from overlapping defects. Experimental results show that the proposed model achieves a mean average precision (mAP)@0.5 of 93.2% and a detection speed of 104.2 frames per second (FPS), outperforming the state-of-the-art (SOTA) YOLOv12-L and EER-DETR. The method achieves an optimal accuracy–speed trade-off, offering an efficient, cost-effective solution for intelligent inspection of large-scale PV plants.

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