Synthetic Data for Defect Segmentation on Complex Metal Surfaces

Fulir, Juraj; Bosnar, Lovro; Hagen, Hans; Gospodnetić, Petra

doi:10.1109/cvprw59228.2023.00465

Cited by 6 publications

(3 citation statements)

References 36 publications

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“…where p s (i) represents the frequency of pixel values equal to i, n i represents the number of pixels with a pixel value of i, and N represents the total number of pixels in the sub-image. Secondly, the ULIE method computes the Cumulative Distribution Function (CDF) c s (i) for each sub-image s in Equation (7), representing the cumulative frequency of pixel values less than or equal to i:…”

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

confidence: 99%

“…The blurring saucepacket leakage segmentation task (BSLST) can alleviate uneven illumination, which aims to identify the pixel-level leakage at the sauce-packet connection [4]. Traditional and most deep learning algorithms have insufficient performance on the BSLST, due to challenges in handling pixel-level classification under uneven illumination conditions, and those algorithms lack sufficient granularity in capturing monotonous features [5][6][7]. The solving of the BSLST can enhance the efficiency of sauce production in the catering industry, which would reduce the miss rate of manual inspections and unleash economic vitality.…”

Section: Introductionmentioning

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

confidence: 99%

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

show abstract

“…This makes them predestined to be used for training machine learning models. Trained on semi-synthetic images, these models were already successfully applied in many contexts such as crack segmentation in concrete [2 , [13] , [14] , [15] and defect segmentation on metal surfaces [12] . Furthermore, segmentation methods – both from classical image processing and machine learning – can be validated objectively [2 , 3] .…”

Section: Introductionmentioning

confidence: 99%