Fibre waviness characterisation and modelling by Filtered Canny Misalignment Analysis (FCMA)

Qi, Songming; Alajarmeh, Omar; Shelley, Tristan; Schubel, Peter; Rendle-Short, Kendric; Zeng, Xuesen

doi:10.1016/j.compstruct.2023.116666

Cited by 7 publications

(2 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional methods rely on expert experience. Songming et al [9] developed an improved detector using the Canny operator, which improve the computational efficiency and increase the precision of fibre identification. Sharma et al [10] used a Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) segmentation method, which integrate with a modified ResNet50 model for brain tumor detection to help clinicians.…”

Section: Introductionmentioning

confidence: 99%

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

You,

Lin,

Feng

et al. 2024

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

You,

Lin,

Feng

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Traditional methods rely on expert experience. Songming et al [9] developed an improved detector using the Canny operator, which improves the computational efficiency and increases the precision of fiber identification. Sharma et al [10] used a Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) segmentation method, which integrates with a modified ResNet50 model for brain tumor detection to help clinicians.…”

Section: Introductionmentioning

confidence: 99%

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

You,

Lin,

Feng

et al. 2024

Sensors

View full text Add to dashboard Cite

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

Formation of non-uniform fibre distributions in winding-pultrusion process and its effect on axial compressive properties of hollow GFRP profiles

Alajarmeh

Shelley

et al. 2023

Composites Part A: Applied Science and Manufacturing

View full text Add to dashboard Cite

Fibre waviness characterisation and modelling by Filtered Canny Misalignment Analysis (FCMA)

Cited by 7 publications

References 38 publications

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

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

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

Formation of non-uniform fibre distributions in winding-pultrusion process and its effect on axial compressive properties of hollow GFRP profiles

Contact Info

Product

Resources

About