Industrial Weak Scratches Inspection Based on Multifeature Fusion Network
Tao, Xian1,2; Zhang, Dapeng1,2; Hou, Wei1,2; Ma, Wenzhi1,2; Xu, De1,2
Source PublicationIEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
ISSN0018-9456
2021
Volume70Issue:1Pages:14
Abstract

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.

KeywordDeep learning defect detection machine vision multiple feature fusion weak scratch inspection
DOI10.1109/TIM.2020.3025642
WOS KeywordCRACK DETECTION ; DEFECTS DETECTION ; CLASSIFICATION ; ACCURATE
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61703399] ; Science Challenge Project[TZ2018006-0204-02] ; Beijing Municipal Natural Science Foundation[4204113]
Funding OrganizationNational Natural Science Foundation of China ; Science Challenge Project ; Beijing Municipal Natural Science Foundation
WOS Research AreaEngineering ; Instruments & Instrumentation
WOS SubjectEngineering, Electrical & Electronic ; Instruments & Instrumentation
WOS IDWOS:000594910700002
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Sub direction classification目标检测、跟踪与识别
Citation statistics
Cited Times:24[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/42771
Collection中科院工业视觉智能装备工程实验室_精密感知与控制
Corresponding AuthorTao, Xian
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Recommended Citation
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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