Defect Segmentation and Detection Method for Injection Molding Products Based on SAM2-U-Net and Semi-supervised Learning

YANG Hongyu; ZHU Xudong; SONG Shuai; ZHANG Sichang; YANG Weimin

doi:10.3969/j.issn.1674-6457.2026.04.021

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Journal of Netshape Forming Engineering ›› 2026, Vol. 18 ›› Issue (4) : 206-212. DOI: 10.3969/j.issn.1674-6457.2026.04.021

Advanced Manufacturing Technology and Equipment

Defect Segmentation and Detection Method for Injection Molding Products Based on SAM2-U-Net and Semi-supervised Learning

YANG Hongyu¹, ZHU Xudong², SONG Shuai², ZHANG Sichang², YANG Weimin^1,*

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Abstract

To enhance the accuracy and robustness of deep learning methods in the detection of injection molding products while reducing their dependence on high-quality labeled data, the work aims to construct a defect detection method that integrates general visual knowledge with task-specific features. Specifically, the Segment Anything Model 2 (SAM2) was utilized to transfer visual priors learned from large-scale natural image datasets. Adapter modules were introduced to enable efficient parameter fine-tuning, allowing the model to capture the distinctive characteristics of injection molding defects. At the same time, the U-Net decoding structure was employed to achieve high-precision semantic segmentation of defect regions. A semi-supervised auxiliary training strategy was adopted, introducing non-defective samples to enhance the model's generalization ability. Compared with mainstream segmentation methods, the proposed defect detection method significantly improved segmentation performance. The mIoU increased by 7.13%, Recall by 7.87%, Pixel Accuracy (PA) by 0.67% and mPrecision by 4.83%. Under the condition of scarce defect samples, the proposed defect detection model combining SAM2 and U-Net, along with a semi-supervised auxiliary training strategy, significantly improves the performance of injection molding product defect detection, reduces reliance on high-quality annotated data, and demonstrates promising application value in the plastics industry.

Key words

injection molding products / defect detection / semantic segmentation / semi-supervised learning / Segment Anything Model 2 (SAM2) / U-Net

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YANG Hongyu, ZHU Xudong, SONG Shuai, ZHANG Sichang, YANG Weimin. Defect Segmentation and Detection Method for Injection Molding Products Based on SAM2-U-Net and Semi-supervised Learning[J]. Journal of Netshape Forming Engineering. 2026, 18(4): 206-212 https://doi.org/10.3969/j.issn.1674-6457.2026.04.021

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