The defect detection of personalized print based on template matching

Ma, Binwu; Zhu, Wei; Wang, Yanghong; Wu, Huan; Yang, Yanzhu; Fan, Hui; Xu, Hongwei

doi:10.1109/icus.2017.8278352

Cited by 13 publications

(4 citation statements)

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“…Next, a twice template matching algorithm was established in paper [11], which firstly matched the template and then performed differential operation on matched image to find the location of the defect. In paper [12], the authors designed a bidirectional image difference algorithm to avoid the error detection of contour artifacts.…”

Section: Related Researchmentioning

confidence: 99%

Industrial Printing Image Defect Detection Using Multi-Edge Feature Fusion Algorithm

et al. 2021

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Online defect detection system is a necessary technical measure and important means for large-scale industrial printing production. It is effective to reduce artificial detection fatigue and improve the accuracy and stability of industry printing line. However, the existing defect detection algorithms are mainly developed based on high-quality database and it is difficult to detect the defects on low-quality printing images. In this paper, we propose a new multi-edge feature fusion algorithm which is effective in solving this problem. Firstly, according to the characteristics of sheet-fed printing system, a new printing image database is established; compared with the existing databases, it has larger translation, deformation, and uneven illumination variation. These interferences make defect detection become more challenging. Then, SIFT feature is employed to register the database. In order to reduce the number of false detections which are caused by the position, deformation, and brightness deviation between the detected image and reference image, multi-edge feature fusion algorithm is proposed to overcome the effects of these disturbances. Lastly, the experimental results of mAP (92.65%) and recall (96.29%) verify the effectiveness of the proposed method which can effectively detect defects in low-quality printing database. The proposed research results can improve the adaptability of visual inspection system on a variety of different printing platforms. It is better to control the printing process and further reduce the number of operators.

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Section: Related Researchmentioning

confidence: 99%

Industrial Printing Image Defect Detection Using Multi-Edge Feature Fusion Algorithm

et al. 2021

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“…Similarly, Hongbin et al [11] proposed a segmentation method by HOG and Local Binary Pattern (LBP), which combines both HOG and LBP features to accurately identify crack anomalies. Binwu et al [12] developed a secondary template matching method, which extracted the Region of Interest (ROI) by using the four-threshold algorithm. However, traditional methods relied on hand-crafted features, and the generalization performance in real scenes is insufficient.…”

Section: Introductionmentioning

confidence: 99%

ISLS: An Illumination-aware Sauce-packet Leakage Segmentation Method

You,

Lin,

Feng

et al. 2024

Preprint

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The segmentation of abnormal regions is vital in smart manufacturing. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby impeding the rapid production of industrial assembly lines. To alleviate this issue, we propose the two-stage Illumination-aware Sauce-packet Leakage Segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: Illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose an image enhancement to relieve the impact of uneven illumination, enhancing the texture details of ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the Multi-scale Feature Fusion Module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by adaptive weighting of spatial and channel dimensions. Furthermore, we generated a self-built dataset of sauce-packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method. Our code is available at https://github.com/LSJ5106/SauceDetect.

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“…Similarly, Hongbin et al [11] proposed a segmentation method by HOG and Local Binary Pattern (LBP), which combines both HOG and LBP features to accurately identify crack anomalies. Binwu et al [12] developed a secondary template-matching method, which extracted the Region of Interest (ROI) by using the four-threshold algorithm. However, traditional methods rely on hand-crafted features, and the generalization performance in real scenes is insufficient.…”

Section: Introductionmentioning

confidence: 99%

ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method

You,

Lin,

Feng

et al. 2024

Sensors

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The segmentation of abnormal regions is vital in smart manufacturing. The blurring sauce-packet leakage segmentation task (BSLST) is designed to distinguish the sauce packet and the leakage’s foreground and background at the pixel level. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby hindering the measurements of leakage area and impeding the automated sauce-packet production. To alleviate this issue, we propose the two-stage illumination-aware sauce-packet leakage segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose image enhancement to relieve the impact of uneven illumination, enhancing the texture details of the ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the multi-scale feature fusion module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by the MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by the adaptive weighting of spatial and channel dimensions. Furthermore, we generate a self-built dataset of sauce packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method.

show abstract

The defect detection of personalized print based on template matching

Cited by 13 publications

References 1 publication

Industrial Printing Image Defect Detection Using Multi-Edge Feature Fusion Algorithm

Industrial Printing Image Defect Detection Using Multi-Edge Feature Fusion Algorithm

ISLS: An Illumination-aware Sauce-packet Leakage Segmentation Method

ISLS: An Illumination-Aware Sauce-Packet Leakage Segmentation Method

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