文献类型：期刊文献

中文题名：基于特征融合与语义引导的药用胶囊表面缺陷检测

英文题名：Surface defect detection for pharmaceutical capsules based on feature fusion and semantic guidance

作者：董豪 李少波 杨静 王军

第一作者：董豪

机构：[1]贵州大学机械工程学院,贵州贵阳550025;[2]贵州理工学院大数据学院,贵州贵阳550025;[3]贵州大学公共大数据国家重点实验室,贵州贵阳550025

第一机构：贵州大学机械工程学院,贵州贵阳550025

年份：2025

卷号：31

期号：1

起止页码：158-170

中文期刊名：计算机集成制造系统

外文期刊名：Computer Integrated Manufacturing Systems

收录：;EI(收录号：20250817891167);北大核心:【北大核心2023】；

基金：国家重点研发计划资助项目(2023YFB3308802);国家自然科学基金资助项目(52275480);贵州省科技资助项目(黔科合平台KXJZ[2024]002,黔教合KY字[2020]005,黔教合KY字[2020]245号);贵阳市科技资助项目(筑科项目[2023]7号)。

语种：中文

中文关键词：深度学习;语义分割;缺陷检测;注意力引导;多尺度融合

外文关键词：deep learning;semantic segmentation;defect detection;attentional guidance;multi-scale fusion

摘要：作为制药产业中常见的药剂容器,胶囊质量与病症的治疗效果以及患者身体的健康状况密切相关。因此,在胶囊生产质量管理流程中,产品质检技术对其批量生产与实际效用有着重要意义。为实现药用胶囊表面缺陷的像素级分割,提出一种基于特征融合与语义引导的药用胶囊表面缺陷检测方法。首先,利用特征融合模块聚集多尺度语义信息,使各级特征得到有效利用,以增强对多类目标以及细小缺陷的分割能力;其次,为缓解编解码过程中特征丢失问题,通过语义引导模块对语义信息进行正确疏导,提升缺陷分割的局部效果;最后,在细化分割模块的作用下,进一步优化表面缺陷的分割细节。在胶囊缺陷数据集上的评估结果表明,相比于众多现有方法,所提方法在多维度的评价指标下(包括精度、速度、模型大小以及训练时长)具有更为平衡的整体性能。
As a common pharmaceutical container in the pharmaceutical industry,the quality of capsules is closely related to the therapeutic effect of the disease and the health status of the patient.The product quality inspection in the quality control process is of great importance to its mass production and practical utility.To realize the pixel-level segmentation of surface defects on pharmaceutical capsules,a surface defect detection method for pharmaceutical capsules based on feature fusion and semantic guidance was proposed.The feature fusion module was used to aggregate multi-scale semantic information to enable effective utilization of features at various levels for enhancing the segmentation ability of multi-class objects and tiny defects.To alleviate the issue of feature lose during coding and decoding,the semantic information was correctly channeled through the semantic guidance module to enhance the local effect of defect segmentation.Finally,the segmentation details of surface defects were further optimized under the role of the refine segmentation module.The evaluation results on the capsule defect dataset showed that the proposed method had a more balanced overall performance under multi-dimensional evaluation metrics including accuracy,speed,model size,and training time compared with existing methods.