LF-RTMDet: an instance segmentation algorithm for real-time detection of water-filled barriers
Water-filled barriers are commonly used traffic control devices, typically employed in road maintenance and regulation scenarios. However, their configuration, formed by interconnected rods, creates a continuous barrier that poses an obstacle for autonomous vehicles. To address this, we selected the...
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| Vydáno v: | The Visual computer Ročník 41; číslo 10; s. 7939 - 7950 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Heidelberg
Springer Nature B.V
01.08.2025
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| Témata: | |
| ISSN: | 0178-2789, 1432-2315 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Water-filled barriers are commonly used traffic control devices, typically employed in road maintenance and regulation scenarios. However, their configuration, formed by interconnected rods, creates a continuous barrier that poses an obstacle for autonomous vehicles. To address this, we selected the RTMDet instance segmentation model as the baseline and proposed a lightweight, fine-grained RTMDet instance segmentation model, called LF-RTMDet. LF-RTMDet incorporates the GhostConv module for lightweight architecture, adds the CARAFE upsampling module to enhance fine-grained segmentation capabilities, and uses the PSA attention mechanism to improve WFB recognition in complex environments. Experimental results show a significant reduction in both the number of parameters and computational complexity, while inference accuracy is significantly improved. Finally, the LF-RTMDet model, after INT8 quantization, achieves a real-time inference speed of 32 FPS on the Jetson Orin Nano, with minimal accuracy loss, meeting the requirements for real-time detection. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 0178-2789 1432-2315 |
| DOI: | 10.1007/s00371-025-03847-3 |