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Industrial Weak Scratches Inspection Based on Multifeature Fusion Network | |
Tao, Xian1,2![]() ![]() ![]() ![]() ![]() | |
发表期刊 | IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
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ISSN | 0018-9456 |
2021 | |
卷号 | 70期号:1页码:14 |
摘要 | Scratches are one of the most common defects in industrial manufacturing. Weak scratches in the industrial environment have an ambiguous edge, low contrast, large span, and unfixed shape, which bring difficulty for automatic defect detection. Recently, many existing visual inspection methods based on deep learning cannot completely and effectively inspect industrial weak scratches due to the lack of discriminative features and sufficient spatial detail. In this article, a novel DeepScratchNet is proposed for automatic weak scratch detection by aggregating rich multidimensional feature for scratch representation. To obtain rich features, a pretrained ResNet block as a feature extractor is proposed in this article. To highlight features of scratch and weaken the noise, an attention feature fusion block (AFB) is proposed, which densely fuses high-level semantic features with low-level detail features using dual-attention mechanism. Due to the long span and connectivity of the weak scratches, a context fusion block (CFB) is proposed to learn the complete context. To further improve the scratch segmentation performance, the auxiliary loss is integrated into the proposed network. The proposed DeepScratchNet outperforms the traditional and other state-of-the-art deep learning-based methods on three given real-world industrial data sets with mIoU over 0.8005, 0.812, and 0.9286. The experimental results demonstrate that DeepScratchNet achieves good generalization capabilities. |
关键词 | Deep learning defect detection machine vision multiple feature fusion weak scratch inspection |
DOI | 10.1109/TIM.2020.3025642 |
关键词[WOS] | CRACK DETECTION ; DEFECTS DETECTION ; CLASSIFICATION ; ACCURATE |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61703399] ; Science Challenge Project[TZ2018006-0204-02] ; Beijing Municipal Natural Science Foundation[4204113] |
项目资助者 | National Natural Science Foundation of China ; Science Challenge Project ; Beijing Municipal Natural Science Foundation |
WOS研究方向 | Engineering ; Instruments & Instrumentation |
WOS类目 | Engineering, Electrical & Electronic ; Instruments & Instrumentation |
WOS记录号 | WOS:000594910700002 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/42771 |
专题 | 中国科学院工业视觉智能装备工程实验室_精密感知与控制 |
通讯作者 | Tao, Xian |
作者单位 | 1.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
第一作者单位 | 精密感知与控制研究中心 |
通讯作者单位 | 精密感知与控制研究中心 |
推荐引用方式 GB/T 7714 | Tao, Xian,Zhang, Dapeng,Hou, Wei,et al. Industrial Weak Scratches Inspection Based on Multifeature Fusion Network[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2021,70(1):14. |
APA | Tao, Xian,Zhang, Dapeng,Hou, Wei,Ma, Wenzhi,&Xu, De.(2021).Industrial Weak Scratches Inspection Based on Multifeature Fusion Network.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,70(1),14. |
MLA | Tao, Xian,et al."Industrial Weak Scratches Inspection Based on Multifeature Fusion Network".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 70.1(2021):14. |
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