Lightweight Detection Model RM-LFPN-YOLO for Rebar Counting
In this study, we propose a novel lightweight detection model for rebar counting, which is rectified mobilenet lightweight feature pyramid network based on YOLO (RM-LFPN-YOLO). The model incorporates a lightweight backbone network that integrates the coordinate attention (CA) mechanism, a lightweigh...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10380575/ |
| _version_ | 1827011542082650112 |
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| author | Haodong Liu Wansheng Cheng Chunwei Li Yaowen Xu Song Fan |
| author_facet | Haodong Liu Wansheng Cheng Chunwei Li Yaowen Xu Song Fan |
| author_sort | Haodong Liu |
| collection | DOAJ |
| description | In this study, we propose a novel lightweight detection model for rebar counting, which is rectified mobilenet lightweight feature pyramid network based on YOLO (RM-LFPN-YOLO). The model incorporates a lightweight backbone network that integrates the coordinate attention (CA) mechanism, a lightweight feature pyramid network (LFPN), and a loss function that combines focal loss and efficient intersection over union (EIOU) loss, all meticulously designed to enhance the model’s performance. Experimental results demonstrate that our improved algorithm, with a mere 25.08M parameters, computes efficiently at 7.60G with an input size of 416 pixels. Additionally, it achieves an impressive average precision (AP) of 99.03% at an IOU of 0.5. The proposed lightweight model can be deployed on embedded devices and achieve efficient rebar detection and counting performance. |
| first_indexed | 2024-03-08T15:35:13Z |
| format | Article |
| id | doaj.art-0e3f91cdf3ca4544bac41da2a66483c1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2025-02-18T13:22:46Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0e3f91cdf3ca4544bac41da2a66483c12024-10-31T23:00:26ZengIEEEIEEE Access2169-35362024-01-01123936394710.1109/ACCESS.2024.334997810380575Lightweight Detection Model RM-LFPN-YOLO for Rebar CountingHaodong Liu0Wansheng Cheng1https://orcid.org/0009-0002-3385-2018Chunwei Li2Yaowen Xu3Song Fan4https://orcid.org/0000-0003-3131-6238School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, ChinaIn this study, we propose a novel lightweight detection model for rebar counting, which is rectified mobilenet lightweight feature pyramid network based on YOLO (RM-LFPN-YOLO). The model incorporates a lightweight backbone network that integrates the coordinate attention (CA) mechanism, a lightweight feature pyramid network (LFPN), and a loss function that combines focal loss and efficient intersection over union (EIOU) loss, all meticulously designed to enhance the model’s performance. Experimental results demonstrate that our improved algorithm, with a mere 25.08M parameters, computes efficiently at 7.60G with an input size of 416 pixels. Additionally, it achieves an impressive average precision (AP) of 99.03% at an IOU of 0.5. The proposed lightweight model can be deployed on embedded devices and achieve efficient rebar detection and counting performance.https://ieeexplore.ieee.org/document/10380575/YOLOattentionLFPNfocal loss |
| spellingShingle | Haodong Liu Wansheng Cheng Chunwei Li Yaowen Xu Song Fan Lightweight Detection Model RM-LFPN-YOLO for Rebar Counting IEEE Access YOLO attention LFPN focal loss |
| title | Lightweight Detection Model RM-LFPN-YOLO for Rebar Counting |
| title_full | Lightweight Detection Model RM-LFPN-YOLO for Rebar Counting |
| title_fullStr | Lightweight Detection Model RM-LFPN-YOLO for Rebar Counting |
| title_full_unstemmed | Lightweight Detection Model RM-LFPN-YOLO for Rebar Counting |
| title_short | Lightweight Detection Model RM-LFPN-YOLO for Rebar Counting |
| title_sort | lightweight detection model rm lfpn yolo for rebar counting |
| topic | YOLO attention LFPN focal loss |
| url | https://ieeexplore.ieee.org/document/10380575/ |
| work_keys_str_mv | AT haodongliu lightweightdetectionmodelrmlfpnyoloforrebarcounting AT wanshengcheng lightweightdetectionmodelrmlfpnyoloforrebarcounting AT chunweili lightweightdetectionmodelrmlfpnyoloforrebarcounting AT yaowenxu lightweightdetectionmodelrmlfpnyoloforrebarcounting AT songfan lightweightdetectionmodelrmlfpnyoloforrebarcounting |