LEM-Detector: An Efficient Detector for Photovoltaic Panel Defect Detection
Source abstract
Abstract
Photovoltaic panel defect detection presents significant challenges due to the wide range of defect scales, diverse defect types, and severe background interference, often leading to a high rate of false positives and missed detections. To address these challenges, this paper proposes the LEM-Detector, an efficient end-to-end photovoltaic panel defect detector based on the transformer architecture. To address the low detection accuracy for Crack and Star crack defects and the imbalanced dataset, a novel data augmentation method, the Linear Feature Augmentation (LFA) module, specifically designed for linear features, is introduced. LFA effectively improves model training performance and robustness. Furthermore, the Efficient Feature Enhancement Module (EFEM) is presented to enhance the receptive field, suppress redundant information, and emphasize meaningful features. To handle defects of varying scales, complementary semantic information from different feature layers is leveraged for enhanced feature fusion. A Multi-Scale Multi-Feature Pyramid Network (MMFPN) is employed to selectively aggregate boundary and category information, thereby improving the accuracy of multi-scale target recognition. Experimental results on a large-scale photovoltaic panel dataset demonstrate that the LEM-Detector achieves a detection accuracy of 94.7% for multi-scale defects, outperforming several state-of-the-art methods. This approach effectively addresses the challenges of photovoltaic panel defect detection, paving the way for more reliable and accurate defect identification systems. This research will contribute to the automatic detection of surface defects in industrial production, ultimately enhancing production efficiency.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Photovoltaic System Optimization Techniques · Photovoltaic Systems and Sustainability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China
School of Medical and Health Engineering, Changzhou University, Changzhou 213164, China
Funding
University-Industry Collaborative Education Program · Jiangsu Industry-University-Research Cooperation Program · Jiangsu Province Graduate Student Research and Practice Innovation Program · Jiangsu Provincial Double-Innovation Doctor Program · Changzhou Science and Technology Program · Changzhou University Extracurricular Innovation and Entrepreneur-ship Fund Program for College Students · Changzhou University Outstanding Undergraduate Graduation Design (Thesis) Cultivation Program
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