Qian Wan

• Bachelor of Engineering
• Huazhong University of Science and Technology

Industrial image anomaly segmentation is pivotal in ensuring the quality inspection of products within intelligent manufacturing systems. Recent research efforts have predominantly focused on deep learning-based approaches to address this challenge. However, unsupervised methods are often susceptible to distribution shifting, while supervised metho...

Image Anomaly Detection is a significant stage for visual quality inspection in intelligent manufacturing systems. According to the assumption that only normal images are available during the training stage, unsupervised methods have been studied recently for image anomaly detection. But anomalous images of small scale can be collected for training...

Unsupervised anomaly segmentation methods based on knowledge distillation have recently been developed and have shown superior segmentation performance. However, little attention has been paid to the overfitting problem caused by the inconsistency between the capacity of a neural network and the amount of knowledge in this scheme. This study propos...

Image anomaly detection is an important stage for automatic visual inspection in intelligent manufacturing systems. The wide-ranging anomalies in images, such as various sizes, shapes, and colors, make automatic visual inspection challenging. Previous work on image anomaly detection has achieved significant advancements. However, there are still li...

Image anomaly detection and segmentation are important for the development of automatic product quality inspection in intelligent manufacturing. Because the normal data can be collected easily and abnormal ones are rarely existent, unsupervised methods based on reconstruction and embedding have been mainly studied for anomaly detection. But the det...

Particle defects on the cathodic copper plate surface always happen due to the immaturity of electrolytic copper processing. The removal of defects mainly depends on their height exceeding the plate and current removal requires manual measurement and operation, which is time-consuming and laborious. To automate the removal process, machine vision-b...

Unsupervised image anomaly detection and segmentation is challenging but important in many fields, such as the defect of product inspection in intelligent manufacturing. The challenge is that, the labeled anomalous data is few and only normal data is available, causing the distribution of the anomaly unknowable. Unsupervised methods based on image-...

Anomaly localization is valuable for improvement of complex production processing in smart manufacturing system. As the distribution of anomalies is unknowable and labeled data is few, unsupervised methods based on convolutional neural network have been studied for anomaly localization. But therere still problems for real industrial applications, i...