Cam-Guided U-Net With Adversarial Regularization For Defect Segmentation

Lin, Dongyun; Li, Yiqun; Prasad, Shitala; Nwe, Tin Lay; Dong, Suh-Yeon; Oo, Zaw Min

doi:10.1109/icip42928.2021.9506582

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…In industrial product quality assessment, defect segmentation, i.e., delineating defective regions from anomalous products, plays a critical role in evaluating the severity of anomaly. To automate the process of defect segmentation, there is a surge of research works on exploiting fully-covolutional networks, e.g., UNet and its variants [1][2][3][4][5], to locate defective regions from 2D images of anomalous products [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

MLSA-UNet: End-to-End Multi-Level Spatial Attention Guided UNet for Industrial Defect Segmentation

Lin

Cheng

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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Defect segmentation from 2D images plays a critical role in industrial product quality assessment. In practice, it is common that there are sufficient normal (defect-free) images but a very limited number of anomalous (defective) images. The existing works proposed several UNet variants (e.g., CAM-UNet) by incorporating normal images into the training process to improve the defect segmentation performance. In this paper, we propose Multi-Level Spatial Attention UNet (MLSA-UNet) to address the industrial defect segmentation task. MLSA-UNet is trained in an end-toend manner to simultaneously classify normal/anomalous images and segment out defective regions from anomalous images. The classification process is conducted by Spatial Attention Learning Module (SALM) to generate multi-level spatial attention maps which are exploited by Spatial Attention Guided Decoding Module (SADM) to provide the guidance in the decoding process of UNet. Extensive experiments on MVTec AD dataset demonstrate the superiority of the proposed MLSA-UNet over multiple state-of-the-art UNet variants on defect segmentation.

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Section: Introductionmentioning

confidence: 99%

MLSA-UNet: End-to-End Multi-Level Spatial Attention Guided UNet for Industrial Defect Segmentation

Lin

Cheng

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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A Coupled Compression Generation Network for Remote-Sensing Images at Extremely Low Bitrates

Pan

Zhang

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Automatic Foreign Matter Segmentation System for Superabsorbent Polymer Powder: Application of Diffusion Adversarial Representation Learning

Chen,

Youh,

Chen

et al. 2024

Mathematics

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In current industries, sampling inspections of the quality of powders, such as superabsorbent polymers (SAPs) still are conducted via visual inspection. The size of samples and foreign matter are around 500 μm, making them difficult for humans to identify. An automatic foreign matter detection system for powder has been developed in the present study. The powder samples can be automatically delivered, distributed, and recycled, and images of them are captured through the hardware of the system, while the identification software of this system was developed based on diffusion adversarial representation learning (DARL). The background image is a foreign-matter-free powder image with an input image size of 1024 × 1024 × 3. Since DARL includes adversarial segmentation, a diffusion process, and synthetic image generation, the DARL model was trained using a diffusion block with the employment of a U-Net attention mechanism and a spatial-adaptation de-normalization (SPADE) layer through the adoption of a loss function from a vanilla generative adversarial network (GAN). This model was then compared with supervised models such as a fully convolutional network (FCN), U-Net, and DeepLABV3+, as well as with an unsupervised Otsu threshold segmentation. It should be noted that only 10% of the training samples were utilized for the DARL to learn and the intersection over union (IoU) of the DARL can reach up to 80.15%, which is much higher than the 59.00%, 53.47%, 49.39%, and 30.08% for the Otsu threshold segmentation, FCN, U-Net, and DeepLABV3+ models. Therefore, the performance of the model developed in the present study would not be degraded due to an insufficient number of samples containing foreign matter. In practical applications, there is no need to collect, label, and design features for a large number of foreign matter samples before using the developed system.

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Cam-Guided U-Net With Adversarial Regularization For Defect Segmentation

Cited by 3 publications

References 9 publications

MLSA-UNet: End-to-End Multi-Level Spatial Attention Guided UNet for Industrial Defect Segmentation

MLSA-UNet: End-to-End Multi-Level Spatial Attention Guided UNet for Industrial Defect Segmentation

A Coupled Compression Generation Network for Remote-Sensing Images at Extremely Low Bitrates

Automatic Foreign Matter Segmentation System for Superabsorbent Polymer Powder: Application of Diffusion Adversarial Representation Learning

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