An Efficient Defect Detection Method for MLCC Based on EHL-DETR Deep Learning Model

Meiyun Chen

doi:10.1109/TIM.2025.3612639

An Efficient Defect Detection Method for MLCC Based on EHL-DETR Deep Learning Model

2025-01-01

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DOI：10.1109/TIM.2025.3612639

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Multilayer ceramic capacitors (MLCCs) may present various shapes, sizes, and subtle defects during manufacturing, which can affect their performance and functionality. However, existing detection methods often fail to meet the precision and speed required for MLCC defect detection. To address these issues, this article builds an automated optical detection system (AODS) and proposes a deep learning model called EHL-DETR for defect identification and localization in MLCC images. First, we propose an efficient gate block (EGB), which enhances spatial feature extraction to improve the model’s multiscale recognition capabilities. Second, the HiLo attention mechanism is introduced to achieve weighted fusion of local and global feature information, enhancing the model’s ability to detect subtle defects. Furthermore, the lightweight multiscale feature pyramid network (LMSFPN) is proposed to enhance the fusion of multiscale features and detect the defect more accurately. The experimental results demonstrate that EHL-DETR performs excellently on the MLCC dataset, achieving mAP50, mAP50–95, precision, and recall of 95.0%, 59.4%, 93.5%, and 90.1%, respectively, and a frame rate of 98.8 frames/s, meeting the real-time defect detection requirements for MLCC.

Keywords:

Deep learning

defect detection

feature extraction and fusion

multilayer ceramic capacitor (MLCC)

An Efficient Defect Detection Method for MLCC Based on EHL-DETR Deep Learning Model

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