SLTM Network: Efficient Application of Lightweight Image Segmentation Technology in Detecting Drivable Areas for Unmanned Line-Marking Machines
Image segmentation plays a crucial role in the roadwork operations of autonomous line-painting machines. However, the limited resources of mobile platforms in intelligent line-painting applications pose a dual challenge of ensuring both accuracy and real-time performance in road segmentation. To add...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10540024/ |
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| author | Chao Wang Xiangkai Chen Bingtao Wang Liang Zhang Bing Liu |
| author_facet | Chao Wang Xiangkai Chen Bingtao Wang Liang Zhang Bing Liu |
| author_sort | Chao Wang |
| collection | DOAJ |
| description | Image segmentation plays a crucial role in the roadwork operations of autonomous line-painting machines. However, the limited resources of mobile platforms in intelligent line-painting applications pose a dual challenge of ensuring both accuracy and real-time performance in road segmentation. To address this issue, this study introduces a lightweight yet efficient image segmentation model, termed the SLTM Network. Central to this network is the lightweight SLTM module, which significantly reduces the model’s parameter count and lowers the computational overhead of the decoder. To enhance the interplay of information at different spatial resolutions, the network incorporates an SE attention-enhanced upsampling module (SAUM) and employs a Spatial Attention Sequence (SAS) unit to improve global environment perception at a low computational cost. Comprehensive experimental evaluations on the Cityscapes dataset demonstrate that the SLTM Network excels in balancing speed and accuracy, achieving an mIoU of 70.5% with only 4.07M parameters and an impressive inference speed of 267.1 FPS. On the embedded device Jetson Xavier NX, it achieves an inference speed of 34.2 FPS. Compared to existing lightweight image segmentation models, the SLTM Network exhibits significant advantages in both processing speed and accuracy, making it particularly suitable for real-time autonomous line-painting machine applications. |
| format | Article |
| id | doaj-art-e8539bab9e6d46979c0eb54c950b446b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e8539bab9e6d46979c0eb54c950b446b2024-11-20T00:00:50ZengIEEEIEEE Access2169-35362024-01-011216900116901210.1109/ACCESS.2024.340060910540024SLTM Network: Efficient Application of Lightweight Image Segmentation Technology in Detecting Drivable Areas for Unmanned Line-Marking MachinesChao Wang0https://orcid.org/0009-0005-9091-1258Xiangkai Chen1https://orcid.org/0009-0006-3380-973XBingtao Wang2Liang Zhang3Bing Liu4https://orcid.org/0009-0001-2153-9852School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, ChinaSchool of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, ChinaSchool of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, ChinaSchool of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, ChinaOffice of Academic Affairs, Shandong University, Weihai, ChinaImage segmentation plays a crucial role in the roadwork operations of autonomous line-painting machines. However, the limited resources of mobile platforms in intelligent line-painting applications pose a dual challenge of ensuring both accuracy and real-time performance in road segmentation. To address this issue, this study introduces a lightweight yet efficient image segmentation model, termed the SLTM Network. Central to this network is the lightweight SLTM module, which significantly reduces the model’s parameter count and lowers the computational overhead of the decoder. To enhance the interplay of information at different spatial resolutions, the network incorporates an SE attention-enhanced upsampling module (SAUM) and employs a Spatial Attention Sequence (SAS) unit to improve global environment perception at a low computational cost. Comprehensive experimental evaluations on the Cityscapes dataset demonstrate that the SLTM Network excels in balancing speed and accuracy, achieving an mIoU of 70.5% with only 4.07M parameters and an impressive inference speed of 267.1 FPS. On the embedded device Jetson Xavier NX, it achieves an inference speed of 34.2 FPS. Compared to existing lightweight image segmentation models, the SLTM Network exhibits significant advantages in both processing speed and accuracy, making it particularly suitable for real-time autonomous line-painting machine applications.https://ieeexplore.ieee.org/document/10540024/Lightweight image segmentationunmanned line-marking machinesdeep learning in autonomous drivingembedded systems optimizationSLTM networkreal-time road segmentation |
| spellingShingle | Chao Wang Xiangkai Chen Bingtao Wang Liang Zhang Bing Liu SLTM Network: Efficient Application of Lightweight Image Segmentation Technology in Detecting Drivable Areas for Unmanned Line-Marking Machines IEEE Access Lightweight image segmentation unmanned line-marking machines deep learning in autonomous driving embedded systems optimization SLTM network real-time road segmentation |
| title | SLTM Network: Efficient Application of Lightweight Image Segmentation Technology in Detecting Drivable Areas for Unmanned Line-Marking Machines |
| title_full | SLTM Network: Efficient Application of Lightweight Image Segmentation Technology in Detecting Drivable Areas for Unmanned Line-Marking Machines |
| title_fullStr | SLTM Network: Efficient Application of Lightweight Image Segmentation Technology in Detecting Drivable Areas for Unmanned Line-Marking Machines |
| title_full_unstemmed | SLTM Network: Efficient Application of Lightweight Image Segmentation Technology in Detecting Drivable Areas for Unmanned Line-Marking Machines |
| title_short | SLTM Network: Efficient Application of Lightweight Image Segmentation Technology in Detecting Drivable Areas for Unmanned Line-Marking Machines |
| title_sort | sltm network efficient application of lightweight image segmentation technology in detecting drivable areas for unmanned line marking machines |
| topic | Lightweight image segmentation unmanned line-marking machines deep learning in autonomous driving embedded systems optimization SLTM network real-time road segmentation |
| url | https://ieeexplore.ieee.org/document/10540024/ |
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