Zhang Mengfan, Liu Weifeng, He Linqing, Wang Di
School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi'an 710016, China.
Sensors (Basel). 2025 Aug 23;25(17):5254. doi: 10.3390/s25175254.
Reconstruction-based unsupervised detection methods have demonstrated strong generalization capabilities in the field of tablet anomaly detection, but there are still problems such as poor reconstruction effect and inaccurate positioning of abnormal areas. To address these problems, this paper proposes an unsupervised iffusion-based ablet efect etection () method. This method uses an Assisted Reconstruction (AR) network to introduce original image information to assist in the reconstruction of abnormal areas, thereby improving the reconstruction effect of the diffusion model. It also uses a Scale Fusion (SF) network and an improved anomaly measurement method to improve the accuracy of abnormal area positioning. Finally, the effectiveness of the algorithm is verified on the tablet dataset. The experimental results show that the algorithm in this paper is superior to the algorithms in the same field, effectively improving the detection accuracy and abnormal positioning accuracy, and performing well in the tablet defect detection task.
基于重建的无监督检测方法在片剂异常检测领域已展现出强大的泛化能力，但仍存在重建效果不佳和异常区域定位不准确等问题。为解决这些问题，本文提出一种基于无监督融合的片剂缺陷检测（）方法。该方法使用辅助重建（AR）网络引入原始图像信息以辅助异常区域的重建，从而提高扩散模型的重建效果。它还使用尺度融合（SF）网络和改进的异常度量方法来提高异常区域定位的准确性。最后，在片剂数据集上验证了该算法的有效性。实验结果表明，本文算法优于同领域算法，有效提高了检测精度和异常定位精度，在片剂缺陷检测任务中表现良好。
