A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation

Zhu, Yuhang; Xu, Zhezhuang; Lin, Ye; Chen, Dan; Ai, Zhijie; Zhang, Hongchuan

doi:10.3390/s24051635

Cited by 3 publications

(5 citation statements)

References 42 publications

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“…In the case of insufficient illumination, the ULIE method is built upon the retinex model [22,23]. The retinex model theory posits that a color image can be decomposed into two primary components: the illumination component (lighting) and the reflection component, as shown in Equation (1).…”

Section: Uneven-light Image Enhancement For Illumination-aware Region...mentioning

confidence: 99%

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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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Section: Uneven-light Image Enhancement For Illumination-aware Region...mentioning

confidence: 99%

“…With the advancement of computer vision technology, leakage segmentation has become crucial for intelligent industrial production [1][2][3]. Sauce-packet leakage segmentation is to determine whether a sauce-packet has leakage [4].…”

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

“…In the case of insufficient illumination, the ULIE method is built upon the retinex model [27,28]. The retinex model theory posits that a color image can be decomposed into two primary components: the illumination component (lighting) and the reflection component, as shown in Equation (1).…”

Section: Uneven-light Image Enhancement For Illumination-aware Region...mentioning

confidence: 99%

“…Additionally, W is the weight matrix, and ▽Tr represents a first-order derivative filter, encompassing both horizontal and vertical directions. Finally, the ULIE method iteratively updates according to the retinex model, being solved to obtain the result image Re(x) in Equation (1). The ULIE method applies BM3D [31] for the denoising optimization of the result image Re(x).…”

Section: Uneven-light Image Enhancement For Illumination-aware Region...mentioning

confidence: 99%

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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.

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Temporal-Quality Ensemble Technique for Handling Image Blur in Packaging Defect Inspection

Son,

Jung,

Kim

2024

Sensors

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Despite achieving numerous successes with surface defect inspection based on deep learning, the industry still faces challenges in conducting packaging defect inspections that include critical information such as ingredient lists. In particular, while previous achievements primarily focus on defect inspection in high-quality images, they do not consider defect inspection in low-quality images such as those containing image blur. To address this issue, we proposed a noble inference technique named temporal-quality ensemble (TQE), which combines temporal and quality weights. Temporal weighting assigns weights to input images by considering the timing in relation to the observed image. Quality weight prioritizes high-quality images to ensure the inference process emphasizes clear and reliable input images. These two weights improve both the accuracy and reliability of the inference process of low-quality images. In addition, to experimentally evaluate the general applicability of TQE, we adopt widely used convolutional neural networks (CNNs) such as ResNet-34, EfficientNet, ECAEfficientNet, GoogLeNet, and ShuffleNetV2 as the backbone network. In conclusion, considering cases where at least one low-quality image is included, TQE has an F1-score approximately 17.64% to 22.41% higher than using single CNN models and about 1.86% to 2.06% higher than an average voting ensemble.

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A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation

Cited by 3 publications

References 42 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

Temporal-Quality Ensemble Technique for Handling Image Blur in Packaging Defect Inspection

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