MSP U-Net: Crack Segmentation for Low-Resolution Images Based on Multi-Scale Parallel Attention U-Net
As the expected lifespans of structures and road approaches, as well as the importance of road maintenance, increase globally, safety inspections have emerged as a crucial task. Nonetheless, the existing crack detection models focus on multi-scale feature loss and performance degradation in learning...
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/24/11541 |
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| author | Joon-Hyeok Kim Ju-Hyeon Noh Jun-Young Jang Hee-Deok Yang |
| author_facet | Joon-Hyeok Kim Ju-Hyeon Noh Jun-Young Jang Hee-Deok Yang |
| author_sort | Joon-Hyeok Kim |
| collection | DOAJ |
| description | As the expected lifespans of structures and road approaches, as well as the importance of road maintenance, increase globally, safety inspections have emerged as a crucial task. Nonetheless, the existing crack detection models focus on multi-scale feature loss and performance degradation in learning various types of cracks. We propose the Multi-Scale Parallel Attention U-Net (MSP U-Net) as a network designed for low-resolution images that considers the irregular characteristics of cracks. MSP U-Net applies a large receptive field flock to an attention U-Net, minimizing feature loss across multiple scales. Using the Crack500 dataset, our network achieved a mean intersection of union (mIoU) of 0.7752, outperforming the existing methods on low-resolution images. |
| format | Article |
| id | doaj-art-9b30d64f63ef4ab8a043d42191cf32c6 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-9b30d64f63ef4ab8a043d42191cf32c62025-08-20T02:00:59ZengMDPI AGApplied Sciences2076-34172024-12-0114241154110.3390/app142411541MSP U-Net: Crack Segmentation for Low-Resolution Images Based on Multi-Scale Parallel Attention U-NetJoon-Hyeok Kim0Ju-Hyeon Noh1Jun-Young Jang2Hee-Deok Yang3Department of Computer Science, Chosun University, Gwangju 61452, Republic of KoreaDepartment of Computer Science, Chosun University, Gwangju 61452, Republic of KoreaDepartment of Computer Science, Chosun University, Gwangju 61452, Republic of KoreaDepartment of Computer Science, Chosun University, Gwangju 61452, Republic of KoreaAs the expected lifespans of structures and road approaches, as well as the importance of road maintenance, increase globally, safety inspections have emerged as a crucial task. Nonetheless, the existing crack detection models focus on multi-scale feature loss and performance degradation in learning various types of cracks. We propose the Multi-Scale Parallel Attention U-Net (MSP U-Net) as a network designed for low-resolution images that considers the irregular characteristics of cracks. MSP U-Net applies a large receptive field flock to an attention U-Net, minimizing feature loss across multiple scales. Using the Crack500 dataset, our network achieved a mean intersection of union (mIoU) of 0.7752, outperforming the existing methods on low-resolution images.https://www.mdpi.com/2076-3417/14/24/11541crack detectiondeep learningsemantic segmentation |
| spellingShingle | Joon-Hyeok Kim Ju-Hyeon Noh Jun-Young Jang Hee-Deok Yang MSP U-Net: Crack Segmentation for Low-Resolution Images Based on Multi-Scale Parallel Attention U-Net Applied Sciences crack detection deep learning semantic segmentation |
| title | MSP U-Net: Crack Segmentation for Low-Resolution Images Based on Multi-Scale Parallel Attention U-Net |
| title_full | MSP U-Net: Crack Segmentation for Low-Resolution Images Based on Multi-Scale Parallel Attention U-Net |
| title_fullStr | MSP U-Net: Crack Segmentation for Low-Resolution Images Based on Multi-Scale Parallel Attention U-Net |
| title_full_unstemmed | MSP U-Net: Crack Segmentation for Low-Resolution Images Based on Multi-Scale Parallel Attention U-Net |
| title_short | MSP U-Net: Crack Segmentation for Low-Resolution Images Based on Multi-Scale Parallel Attention U-Net |
| title_sort | msp u net crack segmentation for low resolution images based on multi scale parallel attention u net |
| topic | crack detection deep learning semantic segmentation |
| url | https://www.mdpi.com/2076-3417/14/24/11541 |
| work_keys_str_mv | AT joonhyeokkim mspunetcracksegmentationforlowresolutionimagesbasedonmultiscaleparallelattentionunet AT juhyeonnoh mspunetcracksegmentationforlowresolutionimagesbasedonmultiscaleparallelattentionunet AT junyoungjang mspunetcracksegmentationforlowresolutionimagesbasedonmultiscaleparallelattentionunet AT heedeokyang mspunetcracksegmentationforlowresolutionimagesbasedonmultiscaleparallelattentionunet |