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作 者:吴江江 汲清波[1,2] 张磊 WU Jiangjiang;JI Qingbo;ZHANG Lei(College of Information and Communication Engineering,Harbin Engineering University,Harbin 150001,China;Key Laboratory of Advanced Marine Communication and Information Technology(Ministry of Industry and Information Technology),Harbin Engineering University,Harbin 150001,China)
机构地区:[1]哈尔滨工程大学信息与通信工程学院,黑龙江哈尔滨150001 [2]哈尔滨工程大学先进船舶通信与信息技术工业和信息化部重点实验室,黑龙江哈尔滨150001
出 处:《应用科技》2024年第4期75-82,共8页Applied Science and Technology
摘 要:针对疫苗玻璃试管缺陷检测中遇到的缺陷形态不规则以及缺陷类间相似度高的问题,提出了一种融合小波域信息的语义分割网络。该网络通过引入离散小波变换分支,实现了小波域信息与卷积主干网络特征图的有效融合,从而提高了对不规则缺陷的分割完整度。在主干网络设计中,加入了动态蛇形卷积,以提升对不规则缺陷分割的细腻度。同时,对主干网络结构进行优化,降低了对计算资源的依赖同时提升了分割精度。通过构建专用的疫苗玻璃试管缺陷分割数据集,与现有的几种卷积语义分割网络和视觉Transformer网络进行了一系列对比实验。实验结果显示,该方法在精度、完整度及细腻度方面均优于所对比的网络,证实了该方法在疫苗玻璃缺陷检测任务中的有效性。Confronting the challenges of irregular defect shapes and high inter-class similarity in vaccine vial defect detection,this study introduces a semantic segmentation network that integrates wavelet domain information.The network incorporates a discrete wavelet transform branch,enabling effective fusion of wavelet domain information with the convolutional backbone's feature maps,thereby enhancing the segmentation integrity of irregular defects.In the design of the backbone network,dynamic snake convolution is introduced to refine the segmentation of irregular defects.Furthermore,the backbone network structure is optimized to reduce computational resource dependence while increasing segmentation precision.A dedicated vaccine vial defect segmentation dataset was constructed for a series of comparative experiments with several existing convolutional semantic segmentation networks and vision Transformer networks.Experimental results demonstrate that the proposed method outperforms the compared networks in accuracy,integrity,and refinement,confirming its effectiveness in the task of vaccine vial defect detection.
关 键 词:疫苗玻璃试管 语义分割 缺陷检测 离散小波变换 小波域 多尺度 膨胀卷积 动态蛇形卷积
分 类 号:TP391.4[自动化与计算机技术—计算机应用技术]
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