Neural Network Pruning for Lightweight Metal Corrosion Image Segmentation Models
Metal corrosion detection is essential for ensuring structural safety and minimizing economic losses. While deep learning (DL)-based image segmentation has improved corrosion detection accuracy and efficiency, its high computational demands hinder deployment on resource-constrained edge devices. Thi...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10969780/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849310482911985664 |
|---|---|
| author | Vincent F. Yu Gemilang Santiyuda Shih-Wei Lin Udjianna S. Pasaribu Yuli Sri Afrianti |
| author_facet | Vincent F. Yu Gemilang Santiyuda Shih-Wei Lin Udjianna S. Pasaribu Yuli Sri Afrianti |
| author_sort | Vincent F. Yu |
| collection | DOAJ |
| description | Metal corrosion detection is essential for ensuring structural safety and minimizing economic losses. While deep learning (DL)-based image segmentation has improved corrosion detection accuracy and efficiency, its high computational demands hinder deployment on resource-constrained edge devices. This study investigates lightweight DL models for corrosion segmentation by applying structured pruning to reduce computational costs while maintaining accuracy. We evaluate five segmentation architectures (U-Net, U-Net++, FPN, LinkNet, and MA-Net) and three pruning strategies (linear, automated gradual pruning, and movement pruning) on two corrosion image datasets (NEA and SSCS). Detailed trade-off analysis between model size, computational cost (MAC), and segmentation performance (IoU) reveals that pruning up to 90% sparsity leads to a <inline-formula> <tex-math notation="LaTeX">$\leq 10\%$ </tex-math></inline-formula> IoU drop on SSCS and <inline-formula> <tex-math notation="LaTeX">$\leq 5\%$ </tex-math></inline-formula> on NEA, demonstrating that significant compression is possible with minimal accuracy loss. However, some architectures (e.g., LinkNet) and pruning strategies (e.g., movement pruning) show significant performance deterioration, suggesting that pruning effectiveness varies across models. These findings provide insights into optimizing corrosion segmentation models for efficient deployment on edge devices, balancing accuracy and resource constraints. |
| format | Article |
| id | doaj-art-1ae929ebcc2445eca286206d823b6dc7 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1ae929ebcc2445eca286206d823b6dc72025-08-20T03:53:42ZengIEEEIEEE Access2169-35362025-01-0113716737168710.1109/ACCESS.2025.356243510969780Neural Network Pruning for Lightweight Metal Corrosion Image Segmentation ModelsVincent F. Yu0https://orcid.org/0000-0001-8975-0606Gemilang Santiyuda1https://orcid.org/0000-0002-4432-6059Shih-Wei Lin2https://orcid.org/0000-0003-1343-0838Udjianna S. Pasaribu3Yuli Sri Afrianti4Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, TaiwanDepartment of Industrial Management, National Taiwan University of Science and Technology, Taipei, TaiwanDepartment of Information Management, Chang Gung University, Taoyuan, TaiwanStatistics Research Group, Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Bandung, IndonesiaStatistics Research Group, Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Bandung, IndonesiaMetal corrosion detection is essential for ensuring structural safety and minimizing economic losses. While deep learning (DL)-based image segmentation has improved corrosion detection accuracy and efficiency, its high computational demands hinder deployment on resource-constrained edge devices. This study investigates lightweight DL models for corrosion segmentation by applying structured pruning to reduce computational costs while maintaining accuracy. We evaluate five segmentation architectures (U-Net, U-Net++, FPN, LinkNet, and MA-Net) and three pruning strategies (linear, automated gradual pruning, and movement pruning) on two corrosion image datasets (NEA and SSCS). Detailed trade-off analysis between model size, computational cost (MAC), and segmentation performance (IoU) reveals that pruning up to 90% sparsity leads to a <inline-formula> <tex-math notation="LaTeX">$\leq 10\%$ </tex-math></inline-formula> IoU drop on SSCS and <inline-formula> <tex-math notation="LaTeX">$\leq 5\%$ </tex-math></inline-formula> on NEA, demonstrating that significant compression is possible with minimal accuracy loss. However, some architectures (e.g., LinkNet) and pruning strategies (e.g., movement pruning) show significant performance deterioration, suggesting that pruning effectiveness varies across models. These findings provide insights into optimizing corrosion segmentation models for efficient deployment on edge devices, balancing accuracy and resource constraints.https://ieeexplore.ieee.org/document/10969780/Computer visiondeep learningimage segmentationmetal corrosionpruning |
| spellingShingle | Vincent F. Yu Gemilang Santiyuda Shih-Wei Lin Udjianna S. Pasaribu Yuli Sri Afrianti Neural Network Pruning for Lightweight Metal Corrosion Image Segmentation Models IEEE Access Computer vision deep learning image segmentation metal corrosion pruning |
| title | Neural Network Pruning for Lightweight Metal Corrosion Image Segmentation Models |
| title_full | Neural Network Pruning for Lightweight Metal Corrosion Image Segmentation Models |
| title_fullStr | Neural Network Pruning for Lightweight Metal Corrosion Image Segmentation Models |
| title_full_unstemmed | Neural Network Pruning for Lightweight Metal Corrosion Image Segmentation Models |
| title_short | Neural Network Pruning for Lightweight Metal Corrosion Image Segmentation Models |
| title_sort | neural network pruning for lightweight metal corrosion image segmentation models |
| topic | Computer vision deep learning image segmentation metal corrosion pruning |
| url | https://ieeexplore.ieee.org/document/10969780/ |
| work_keys_str_mv | AT vincentfyu neuralnetworkpruningforlightweightmetalcorrosionimagesegmentationmodels AT gemilangsantiyuda neuralnetworkpruningforlightweightmetalcorrosionimagesegmentationmodels AT shihweilin neuralnetworkpruningforlightweightmetalcorrosionimagesegmentationmodels AT udjiannaspasaribu neuralnetworkpruningforlightweightmetalcorrosionimagesegmentationmodels AT yulisriafrianti neuralnetworkpruningforlightweightmetalcorrosionimagesegmentationmodels |