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<p>Electromagnetic tomography (EMT) is a promising nondestructive imaging technique for metallic defect detection. However, its inherent soft-field diffusion, rapid sensitivity decay, and nonlinear eddy-current interaction lead to severe boundary blurring and low-resolution reconstructions, particularly for small or multiple defects. To address these limitations, this study proposes Edge-DeepLabv3+, a physics-informed deep reconstruction network specifically designed for metallic EMT. The model integrates SE-Res2Net for multi-scale conductivity encoding, DenseASPP for dense receptive-field expansion under strong diffusion, ESA for capturing long-range electromagnetic correlations, and a Boundary-Refinement (BR) decoder to compensate for soft-field-induced edge loss. Furthermore, we introduce an Edge-Focused Hybrid Loss (EHL), which combines global MSE, imbalance-aware Dice loss, and a boundary-supervision BCE applied to morphology-derived defect contours, enabling precise recovery of high-frequency conductivity discontinuities. A physics-based dataset comprising 12,960 samples is generated using COMSOL, incorporating coil misalignment, temperature drift, non-white noise, and mutual-coupling perturbations through domain randomization, ensuring robustness against practical domain shifts. Extensive experiments on both simulation and real EMT systems demonstrate that Edge-DeepLabv3+ significantly improves reconstruction accuracy, boundary fidelity, and robustness to noise compared with LBP, Tikhonov, SE-Res2Net, and DeepLabv3+. The proposed model achieves accurate reconstruction of 3–6 mm single and multiple defects, even under low-SNR (10 dB) conditions, highlighting its strong potential for reliable online metallic defect monitoring in industrial environments.</p>

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Defect Detection of Steel Plates by Electromagnetic Tomography Imaging Based on Edge-DeepLabv3+

  • 期刊论文

  • 发表日期：

    2026年1月22日

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  • Xianglong Liu,
  • Kun Zhang,
  • Ying Wang,
  • Huilin Feng,
  • Nan Wang

摘要

Electromagnetic tomography (EMT) is a promising nondestructive imaging technique for metallic defect detection. However, its inherent soft-field diffusion, rapid sensitivity decay, and nonlinear eddy-current interaction lead to severe boundary blurring and low-resolution reconstructions, particularly for small or multiple defects. To address these limitations, this study proposes Edge-DeepLabv3+, a physics-informed deep reconstruction network specifically designed for metallic EMT. The model integrates SE-Res2Net for multi-scale conductivity encoding, DenseASPP for dense receptive-field expansion under strong diffusion, ESA for capturing long-range electromagnetic correlations, and a Boundary-Refinement (BR) decoder to compensate for soft-field-induced edge loss. Furthermore, we introduce an Edge-Focused Hybrid Loss (EHL), which combines global MSE, imbalance-aware Dice loss, and a boundary-supervision BCE applied to morphology-derived defect contours, enabling precise recovery of high-frequency conductivity discontinuities. A physics-based dataset comprising 12,960 samples is generated using COMSOL, incorporating coil misalignment, temperature drift, non-white noise, and mutual-coupling perturbations through domain randomization, ensuring robustness against practical domain shifts. Extensive experiments on both simulation and real EMT systems demonstrate that Edge-DeepLabv3+ significantly improves reconstruction accuracy, boundary fidelity, and robustness to noise compared with LBP, Tikhonov, SE-Res2Net, and DeepLabv3+. The proposed model achieves accurate reconstruction of 3–6 mm single and multiple defects, even under low-SNR (10 dB) conditions, highlighting its strong potential for reliable online metallic defect monitoring in industrial environments.