连续太赫兹成像在含能材料中金属异物无损检测的应用研究OA

Leveraging the non-contact nature and penetration capability of continuous-wave terahertz imaging,this study pro-posed a nondestructive detection method for metallic foreign objects in energetic materials.Terahertz imaging experiments were conducted on four types of metallic foreign objects covered by energetic-material layers with thicknesses of 0.5-2.0 cm,and a corresponding terahertz image dataset was established.Based on this dataset,a lightweight recognition model based on YOLO26-Partial was developed for metallic foreign object detection.In addition,an enhanced generative adversarial network,termed EDGAN,was proposed for terahertz image super-resolution reconstruction by integrating an attention mechanism with a deep residual structure.Experimental results demonstrate that the proposed recognition algorithm achieves an average recogni-tion accuracy of 99%for the four types of metallic foreign objects while reducing model complexity.Compared with ESRGAN,the proposed reconstruction algorithm achieves average improvements of 5.70%and 1.36%in peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM),respectively.The proposed method enables both accurate recognition and high-quality super-resolution reconstruction of metallic foreign objects in energetic materials,providing a feasible technical approach for non-destructive inspection in related applications.