Search Results - "Lv, Chengkan"

A Fast Surface Defect Detection Method Based on Background Reconstruction by Lv, Chengkan, Zhang, Zhengtao, Shen, Fei, Zhang, Feng, Su, Hu

“…In this paper, we propose an unsupervised background reconstruction method to detect defects on surfaces with unevenly distributed textures. An improved deep…”
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Mask-Guided Generation Method for Industrial Defect Images with Non-uniform Structures by Wei, Jing, Zhang, Zhengtao, Shen, Fei, Lv, Chengkan

“…Defect generation is a crucial method for solving data problems in industrial defect detection. However, the current defect generation methods suffer from the…”
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Dual Branch Learning with Prior Information for Surface Anomaly Detection by Wang, Shuyuan, Lv, Chengkan, Zhang, Zhengtao, Wei, Xueyan

“…Visual surface anomaly detection focuses on the classification and location of regions that deviate from the normal appearance, and generally, only normal…”
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Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection by Chen, Qiyu, Luo, Huiyuan, Gao, Han, Lv, Chengkan, Zhang, Zhengtao

“…Unsupervised anomaly detection methods can identify surface defects in industrial images by leveraging only normal samples for training. Due to the risk of…”
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Produce Once, Utilize Twice for Anomaly Detection by Wang, Shuyuan, Li, Qi, Luo, Huiyuan, Lv, Chengkan, Zhang, Zhengtao

“…Visual anomaly detection aims at classifying and locating the regions that deviate from the normal appearance. Embedding-based methods and reconstruction-based…”
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A Novel Pixel-Wise Defect Inspection Method Based on Stable Background Reconstruction by Lv, Chengkan, Shen, Fei, Zhang, Zhengtao, Xu, De, He, Yonghao

“…In this article, an anomaly detection method based on background reconstruction is proposed to perform defect inspection on the texture surface of the…”
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A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization by Chen, Qiyu, Luo, Huiyuan, Lv, Chengkan, Zhang, Zhengtao

“…Anomaly synthesis strategies can effectively enhance unsupervised anomaly detection. However, existing strategies have limitations in the coverage and…”
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Produce Once, Utilize Twice for Anomaly Detection by Wang, Shuyuan, Li, Qi, Luo, Huiyuan, Lv, Chengkan, Zhang, Zhengtao

“…Visual anomaly detection aims at classifying and locating the regions that deviate from the normal appearance. Embedding-based methods and reconstruction-based…”
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Unsupervised Automatic Defect Inspection based on Image Matching and Local One-class Classification by Lv, Chengkan, Zhang, Zhengtao, Shen, Fei, Zhang, Feng

“…In this paper, an unsupervised defect inspection method based on anomaly detection is proposed to inspect various kinds of surface defects in the field of…”
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Patterned Fabric Defect Detection Based on Double-branch Parallel Improved Faster-RCNN by Wang, Shaohu, Lv, Chengkan, Wang, ShuYuan, Zhang, Zhengtao, Shang, Xiuqin

“…In the weaving of patterned fabrics, surface defects are the main factor affecting quality of fabrics. Due to the low efficiency of manual detection, a deep…”
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Diversified and Multi-Class Controllable Industrial Defect Synthesis for Data Augmentation and Transfer by Wei, Jing, Shen, Fei, Lv, Chengkan, Zhang, Zhengtao, Zhang, Feng, Yang, Huabin

“…Data augmentation is crucial to solve few-sample issues in industrial inspection based on deep learning. However, current industrial data augmentation methods…”
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The Second-place Solution for CVPR VISION 23 Challenge Track 1 -- Data Effificient Defect Detection by Tao, Xian, Qu, Zhen, Luo, Hengliang, Han, Jianwen, He, Yonghao, Liu, Danfeng, Lv, Chengkan, Shen, Fei, Zhang, Zhengtao

“…The Vision Challenge Track 1 for Data-Effificient Defect Detection requires competitors to instance segment 14 industrial inspection datasets in a…”
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A Brief Research and Lookback of the Development of Iris Recognition by Li, Xiaoshuang, Hu, Shenhua, Lv, Chengkan, Qin, Haojun, Zhu, Fenghua, Wang, Feiyue

“…Personal identification has been used more and more widely in information society. Iris recognition, as one of numerous identification methods, has already…”
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