Common strong noise interferences like metal splashes,smoke,and arc light during welding can seriously pollute the laser stripe images,causing the tracking model to drift and leading to tracking failure.At present,the...Common strong noise interferences like metal splashes,smoke,and arc light during welding can seriously pollute the laser stripe images,causing the tracking model to drift and leading to tracking failure.At present,there are already many mature methods for identifying and extracting feature points of linear laser stripes.When the laser stripe forms a curved shape on the surface of the workpiece,these linear methods will no longer be applicable.To eliminate interference sources,enhance the robustness of the weld tracking model,and effectively extract the feature points of curved laser stripes under strong noise conditions.This paper proposes a Conditional Generative Adversarial Network(CGAN)based anti-interference recognition method for welding images.The generator adopts an improved U-Net++structure,adds a Multi-scale Channel Attention module(MS-CAM),introduces Deep Supervision,and proposes a Multi-output Fusion strategy(MOFS)in the output result to en-hance the image inpainting effect;the discriminator uses PatchGAN.The center of the laser stripe is obtained using the grayscale center of mass method and then combined with polynomial fitting to extract the feature points of the weld seam.The experimental results show that the PSNR of the inpainting image is 26.24 dB,the SSIM is 0.98,and the LPIPS is 0.032.The centerline of the inpainting image and the centerline of the noise-free image laser stripe are fitted with a curve.The error of centerline feature points is no more than 5%,confirming the superiority and feasibility of the method.展开更多
With the advancement of electronic countermeasures,airborne synthetic aperture radar(SAR)systems are facing increasing challenges in maintaining effective performance in hostile environments.In particular,high-power i...With the advancement of electronic countermeasures,airborne synthetic aperture radar(SAR)systems are facing increasing challenges in maintaining effective performance in hostile environments.In particular,high-power interference can severely degrade SAR imaging and signal processing,often rendering target detection impossible.This highlights the urgent need for robust anti-interference solutions in both the signal processing and image processing domains.While current methods address interference across various domains,techniques such as waveform modification and spatial filtering typically increase the system costs and complexity.To overcome these limitations,we propose a novel approach that leverages the multi-domain characteristics of interference to efficiently suppress narrowband interference and repeater modulation interference.Specifically,narrowband interference is mitigated using notch filtering,a signal processing technique that effectively filters out unwanted frequencies,while repeater modulation interference is addressed through strong signal amplitude normalization,which enhances both the signal and image processing quality.These methods were validated through tests on real SAR data,demonstrating significant improvements in the imaging performance and system robustness.Our approach offers valuable insights for advancing anti-interference technologies in SAR systems and provides a cost-effective solution to enhance their resilience in complex electronic warfare environments.展开更多
Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To ...Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.展开更多
基金Supported by the"The 14th Five Year Plan"Hubei Provincial ad-vantaged characteristic disciplines(groups)project of Wuhan University of Science and Technology(Grant No.2023B0404)National Natural Science Foundation of China(Grant Nos.52275503 and 72471181)+2 种基金Hubei Provincial Outstanding Youth Fund of China(Grant No.2023AFA092)Hubei Provincial Natural Science Foundation of China(Grant No.2023AFB915)Hubei Provincial Key Research and Development Plan Project of China(Grant No.2023BAB048).
文摘Common strong noise interferences like metal splashes,smoke,and arc light during welding can seriously pollute the laser stripe images,causing the tracking model to drift and leading to tracking failure.At present,there are already many mature methods for identifying and extracting feature points of linear laser stripes.When the laser stripe forms a curved shape on the surface of the workpiece,these linear methods will no longer be applicable.To eliminate interference sources,enhance the robustness of the weld tracking model,and effectively extract the feature points of curved laser stripes under strong noise conditions.This paper proposes a Conditional Generative Adversarial Network(CGAN)based anti-interference recognition method for welding images.The generator adopts an improved U-Net++structure,adds a Multi-scale Channel Attention module(MS-CAM),introduces Deep Supervision,and proposes a Multi-output Fusion strategy(MOFS)in the output result to en-hance the image inpainting effect;the discriminator uses PatchGAN.The center of the laser stripe is obtained using the grayscale center of mass method and then combined with polynomial fitting to extract the feature points of the weld seam.The experimental results show that the PSNR of the inpainting image is 26.24 dB,the SSIM is 0.98,and the LPIPS is 0.032.The centerline of the inpainting image and the centerline of the noise-free image laser stripe are fitted with a curve.The error of centerline feature points is no more than 5%,confirming the superiority and feasibility of the method.
文摘With the advancement of electronic countermeasures,airborne synthetic aperture radar(SAR)systems are facing increasing challenges in maintaining effective performance in hostile environments.In particular,high-power interference can severely degrade SAR imaging and signal processing,often rendering target detection impossible.This highlights the urgent need for robust anti-interference solutions in both the signal processing and image processing domains.While current methods address interference across various domains,techniques such as waveform modification and spatial filtering typically increase the system costs and complexity.To overcome these limitations,we propose a novel approach that leverages the multi-domain characteristics of interference to efficiently suppress narrowband interference and repeater modulation interference.Specifically,narrowband interference is mitigated using notch filtering,a signal processing technique that effectively filters out unwanted frequencies,while repeater modulation interference is addressed through strong signal amplitude normalization,which enhances both the signal and image processing quality.These methods were validated through tests on real SAR data,demonstrating significant improvements in the imaging performance and system robustness.Our approach offers valuable insights for advancing anti-interference technologies in SAR systems and provides a cost-effective solution to enhance their resilience in complex electronic warfare environments.
文摘印刷电路板(Printed Circuit Board,PCB)缺陷会造成巨额经济损失与安全隐患,传统的检测方法精度和效率都较为低下,现有的深度学习模型在面对复杂背景下的小目标检测时存在明显的不足。文章针对YOLOv10在PCB中的检测性能不足,在主干网络采用SPD-Conv模块替代传统卷积,通过维度重排保留小目标的特征并且降低背景干扰。在颈部网络的C2f模块中嵌入SE注意力机制,构建C2f_SE模块提升特征区分度。文章在北京大学PCB数据集的基础上,通过镜像、旋转等数据增强后将数据集扩展至6930张。实验结果表明,改进模型平均精度均值(mean Average Precision,mAP)达98.1%,较原始YOLOv10提升4.7%,其中鼠咬、毛刺等小目标缺陷检测精度提升明显。该模型为工业场景PCB缺陷检测提供了高效可靠方案。
文摘Defect detection in printed circuit boards(PCB)remains challenging due to the difficulty of identifying small-scale defects,the inefficiency of conventional approaches,and the interference from complex backgrounds.To address these issues,this paper proposes SIM-Net,an enhanced detection framework derived from YOLOv11.The model integrates SPDConv to preserve fine-grained features for small object detection,introduces a novel convolutional partial attention module(C2PAM)to suppress redundant background information and highlight salient regions,and employs a multi-scale fusion network(MFN)with a multi-grain contextual module(MGCT)to strengthen contextual representation and accelerate inference.Experimental evaluations demonstrate that SIM-Net achieves 92.4%mAP,92%accuracy,and 89.4%recall with an inference speed of 75.1 FPS,outperforming existing state-of-the-art methods.These results confirm the robustness and real-time applicability of SIM-Net for PCB defect inspection.
