# Lem-Detector: An Efficient Detector for Photovoltaic Panel Defect Detection
## 5 Conclusions  
This paper presents the LEM-Detector, an efficient end-to-end detector for photovoltaic panel defect detection. The proposed method addresses several challenges in this domain, including the wide range of defect scales, diverse defect types, and severe background interference. Our approach aims to enhance detection accuracy and robustness by introducing several novel components: the LFA, the EFEM, and the MMFPN.  
The LFA module addresses the issue of imbalanced training data by focusing on augmenting linear feature samples using a cosine similarity function. This targeted augmentation strategy effectively improves the model's ability to detect small-scale defects like cracks and star cracks. The EFEM module is designed to enhance the receptive field and capture subtle texture information, which is crucial for identifying defects amidst complex backgrounds. The MMFPN facilitates the aggregation of complementary information from different feature layers, ensuring that both high-level category features and edge information are effectively utilized.  
The experimental results demonstrate the effectiveness of the proposed LEM-Detector. On the PVEL-AD dataset, our model achieves an mAP of 94.7%, outperforming several state  
Figure 11: Heatmaps of the feature fusion stage.  
of-the-art methods. The improvements are particularly significant for the detection of Crack and Star crack defects, highlighting the effectiveness of the LFA module. Additionally, the EFEM and MMFPN contribute to the overall enhancement of multi-scale defect detection by preserving detailed information and improving feature fusion.  
The aim and assumptions of this study have been achieved through the introduction of these novel components, which together address the challenges in photovoltaic panel defect detection. The results of this research are expected to be useful for industrial applications in the photovoltaic sector, enabling more reliable and accurate defect identification systems.  
For future research directions, we plan to explore methods for automating the threshold generation in the LFA module to further streamline the training process. Additionally, we aim to develop a more lightweight model to enhance the practicality and deployability of the LEM-Detector in real-world scenarios. Overall, this work provides valuable technical references for visual inspection tasks in similar industrial applications.  
Conceptualization, X.Z., X.L. and R.W.; Methodology, X.Z. and X.L.; Project administration, R.W.; Validation, X.Z., X.L. and W.H.; Writing--original draft, X.Z., X.L. and R.W.; Writing--review and editing, X.Z., X.L. and R.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University-Industry Collaborative Education Program, grant number 220605181012316, Jiangsu Industry-University-Research Cooperation Program, grant number BY20230265, Jiangsu Province Graduate Student Research and Practice Innovation Program, grant number YPC22020142 and YPC23020155, Jiangsu Provincial Double-Innovation Doctor Program, grant number JSSCBS20210908 and JSSCBS20210896, Changzhou Science and Technology Program, grant number CJ20235041, Changzhou University Extracurricular Innovation and Entrepreneur-Ship Fund Program for College Students, grant number QZX202020187 and Changzhou University Outstanding Undergraduate Graduation Design (Thesis) Cultivation Program, grant number QZX23020036 and QZX24020101.  
Not applicable.  
The data presented in this study are openly available in: [[http://aihebut.com/col.jsp?id=118](http://aihebut.com/col.jsp?id=118)]([http://aihebut.com/col.jsp?id=118](http://aihebut.com/col.jsp?id=118)) (accessed on 6 November 2024).  
The authors declare no conflicts of interest.