深圳大学学报（理工版）2026，Vol.43Issue(2)：171-178,8.DOI:10.3724/SP.J.1249.2026.02171
基于CNN-ViT融合与特征增强的金刚石刀头结块缺陷检测
CNN-ViT fusion with feature enhancement for defect detection of diamond tool segments
摘要
Abstract
Existing deep learning-based industrial defect detection methods primarily focus on workpieces with regular textures and uniform imaging conditions,while insufficient attention has been paid to highly reflective alloy components with complex surface reflections and textural interference.Owing to the presence of diamond particles and metal powders,their surfaces exhibit strong specular reflections and random highlights,causing scratches,holes,edge defects,and other imperfections to be highly entangled with background noise and thus significantly increasing detection difficulty.To address this challenge,a cross-modal dynamic fusion framework,termed DCVNet,is proposed for defect detection on complex,highly reflective composite material surfaces.A local-global feature decoupling mechanism is constructed to separate defect-related information from reflection-induced background interference.A multi-stage defect-enhanced clustering algorithm is designed to achieve physical prior separation of background and defects.Furthermore,a progressive feature fusion module is introduced to realize deep cross-scale feature fusion between convolutional neural network(CNN)and vision transformer(ViT).A dedicated surface defect image dataset of diamond tool segments was constructed to train the model.Persuasive experiments were conducted by comparing DCVNet with GoogLeNet,ResNet50,ResNet101,ViT-L16,MobileNetV2,CRAD,PNI,SuperSimpleNet,and MAML models.The results demonstrate that the DCVNet model achieves a detection accuracy of 0.841 and a recall rate of 0.866,outperforming the comparison models.The proposed DCVNet model exhibits strong robustness and high detection performance for defects on complex,highly reflective composite material surfaces,providing an effective solution for industrial defect inspection scenarios.关键词
计算机视觉/工业品缺陷检测/卷积神经网络/视觉变换器/自注意力/特征融合/金刚石刀头结块Key words
computer vision/industrial defect detection/convolutional neural network/vision transformer/self-attention/feature fusion/diamond tool segments分类
信息技术与安全科学引用本文复制引用
赵楠楠,赵文龙,张海刚,匡国文,何玉林..基于CNN-ViT融合与特征增强的金刚石刀头结块缺陷检测[J].深圳大学学报（理工版）,2026,43(2):171-178,8.基金项目
National Natural Science Foundation of China(62272320) （62272320）
Research Projects of Department of Education of Guangdong Province(2023KCXTD077) （2023KCXTD077）
Science and Technology Major Project of Shenzhen(KJZD20230923114809020) （KJZD20230923114809020）
Scientific Research Startup Fund for Shenzhen High-Caliber Personnel of SZPU(6021310030K) 国家自然科学基金资助项目(62272320) （6021310030K）
广东省教育厅重点领域专项资助项目(2023KCXTD077) （2023KCXTD077）
深圳市科技重大专项资助项目(KJZD20230923114809020) （KJZD20230923114809020）
深圳职业技术大学高层次人才科研启动资助项目(6021310030K) （6021310030K）
