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

• Published in: Mathematics (Basel) 2024-08, Vol.12 (16), p.2473
• Main Authors: Chen, Ssu-Han, Youh, Meng-Jey, Chen, Yan-Ru, Jang, Jer-Huan, Chen, Hung-Yi, Cao, Hoang-Giang, Hsueh, Yang-Shen, Liu, Chuan-Fu, Liu, Kevin Fong-Rey
• Format: Article
• Language: English
• Subjects: Accuracy; Deep learning; diffusion adversarial representation learning; Diffusion barriers; Diffusion layers; foreign matter; Generative adversarial networks; Image processing; Image segmentation; Neural networks; Performance degradation; Polymerization; Radiation; Representations; Spectrum analysis; Superabsorbent polymers; Support vector machines; Vision systems
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math12162473

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.