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Vision01:24

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Leveraging Vision Foundation Model via PConv-Based Fine-Tuning with Automated Prompter for Defect Segmentation.

Yifan Jiang1, Jinshui Chen1, Jiangang Lu1

  • 1State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China.

Sensors (Basel, Switzerland)
|April 26, 2025
PubMed
Summary
This summary is machine-generated.

PA-SAM enhances industrial defect segmentation by adapting the Segment Anything Model (SAM) using parameter-efficient fine-tuning and an automated prompt generation system. This framework improves accuracy and scalability for identifying defects in industrial images.

Keywords:
automated prompterdefect segmentationlow-rank adaptationparameter-efficient fine-tuningpartial convolutionsegment anything modelvision foundation model

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Industrial Automation

Background:

  • Image segmentation is critical for industrial defect detection.
  • Foundation models like Segment Anything Model (SAM) offer strong generalization but face challenges in industrial settings due to feature discrepancies and reliance on manual prompts.
  • Existing SAM applications struggle with scalability in specialized industrial environments.

Purpose of the Study:

  • To develop an industrial defect segmentation framework, PA-SAM, that overcomes the limitations of SAM for industrial applications.
  • To enhance SAM's performance on industrial defect datasets through specialized fine-tuning and automated prompt generation.
  • To adapt SAM into an end-to-end semantic segmentation solution for industrial defect identification.

Main Methods:

  • Introduced Multi-Scale Partial Convolution Aggregation (MSPCA) within Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning (PEFT) to adapt the image encoder to industrial defect characteristics.
  • Developed an Image-to-Prompt Embedding Generator (IPEG) to autonomously create high-quality prompt embeddings from image embeddings, removing the need for manual prompts.
  • Refined SAM's mask decoder to create an end-to-end semantic segmentation framework.

Main Results:

  • PA-SAM achieved a mean Intersection over Union (IoU) of 73.87% and 68.30% on two real-world industrial defect datasets.
  • The framework obtained mean Dice coefficients of 84.90% and 80.22%, outperforming state-of-the-art algorithms.
  • Demonstrated robust generalization capabilities and significant application potential in industrial defect segmentation.

Conclusions:

  • PA-SAM effectively addresses the limitations of SAM for industrial defect segmentation by integrating PEFT with MSPCA-LoRA and IPEG.
  • The proposed framework significantly improves segmentation accuracy and efficiency, offering a scalable solution for industrial defect identification.
  • PA-SAM shows strong potential for real-world deployment in industrial quality control and inspection.