SimSPPF module structure.
<div><p>Millimeter-wave (mmWave) radar has become an important research direction in the field of object detection because of its characteristics of all-time, low cost, strong privacy and not affected by harsh weather conditions. Therefore, the research on millimeter wave radar object de...
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2025
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| _version_ | 1852016415946571776 |
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| author | Mengqi Yuan (4852555) |
| author2 | Yajing Yuan (22278948) Xiangqun Zhang (9183983) Zhenghao Zhu (8812391) Chenxi Zhao (5557568) Xiangqian Gao (6109775) Genyuan Du (22278951) |
| author2_role | author author author author author author |
| author_facet | Mengqi Yuan (4852555) Yajing Yuan (22278948) Xiangqun Zhang (9183983) Zhenghao Zhu (8812391) Chenxi Zhao (5557568) Xiangqian Gao (6109775) Genyuan Du (22278951) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mengqi Yuan (4852555) Yajing Yuan (22278948) Xiangqun Zhang (9183983) Zhenghao Zhu (8812391) Chenxi Zhao (5557568) Xiangqian Gao (6109775) Genyuan Du (22278951) |
| dc.date.none.fl_str_mv | 2025-09-19T21:26:24Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0332931.g005 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/SimSPPF_module_structure_/30169068 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological Sciences not elsewhere classified Information Systems not elsewhere classified three main improvements strengthen feature extraction recover fine details optimize feature fusion novel backbone network model &# 8217 mean average precision harsh weather conditions great practical significance 500 annotated images target detection scene new target detection xlink "> millimeter publicly available 5 map ), precision important research direction 6 %, respectively mmwave yolov8n model object detection mmwave yolov8n detection algorithm strong privacy sampling technique results show poor performance method evaluation low cost intelligent security including 2 deep learning data modeling classification framework |
| dc.title.none.fl_str_mv | SimSPPF module structure. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>Millimeter-wave (mmWave) radar has become an important research direction in the field of object detection because of its characteristics of all-time, low cost, strong privacy and not affected by harsh weather conditions. Therefore, the research on millimeter wave radar object detection is of great practical significance for applications in the field of intelligent security and transportation. However, in the multi-target detection scene, millimeter wave radar still faces some problems, such as unable to effectively distinguish multiple objects and poor performance of detection algorithm. Focusing on the above problems, a new target detection and classification framework of S2DB-mmWave YOLOv8n, based on deep learning, is proposed to realize more accuracy. There are three main improvements. First, a novel backbone network was designed by incorporating new convolutional layers and the Simplified Spatial Pyramid Pooling - Fast (SimSPPF) module to strengthen feature extraction. Second, a dynamic up-sampling technique was introduced to improve the model’s ability to recover fine details. Finally, a bidirectional feature pyramid network (BiFPN) was integrated to optimize feature fusion, leveraging a bidirectional information transfer mechanism and an adaptive feature selection strategy. A publicly available 5-class object mmWave radar heatmap dataset, including 2,500 annotated images, were selected for data modeling and method evaluation. The results show that the mean average precision (mAP), precision and recall of the S2DB-mmWave YOLOv8n model were 93.1% mAP@0.5, 55.8% mAP@0.5:0.95, 89.4% and 90.6%, respectively, which is 3.3, 1.6, 4.5 and 7.7 percentage points higher than the baseline YOLOv8n network without increasing the parameter count.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_0a104e543bc0600a914718c63cf2edad |
| identifier_str_mv | 10.1371/journal.pone.0332931.g005 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30169068 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | SimSPPF module structure.Mengqi Yuan (4852555)Yajing Yuan (22278948)Xiangqun Zhang (9183983)Zhenghao Zhu (8812391)Chenxi Zhao (5557568)Xiangqian Gao (6109775)Genyuan Du (22278951)Biological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedthree main improvementsstrengthen feature extractionrecover fine detailsoptimize feature fusionnovel backbone networkmodel &# 8217mean average precisionharsh weather conditionsgreat practical significance500 annotated imagestarget detection scenenew target detectionxlink "> millimeterpublicly available 5map ), precisionimportant research direction6 %, respectivelymmwave yolov8n modelobject detectionmmwave yolov8ndetection algorithmstrong privacysampling techniqueresults showpoor performancemethod evaluationlow costintelligent securityincluding 2deep learningdata modelingclassification framework<div><p>Millimeter-wave (mmWave) radar has become an important research direction in the field of object detection because of its characteristics of all-time, low cost, strong privacy and not affected by harsh weather conditions. Therefore, the research on millimeter wave radar object detection is of great practical significance for applications in the field of intelligent security and transportation. However, in the multi-target detection scene, millimeter wave radar still faces some problems, such as unable to effectively distinguish multiple objects and poor performance of detection algorithm. Focusing on the above problems, a new target detection and classification framework of S2DB-mmWave YOLOv8n, based on deep learning, is proposed to realize more accuracy. There are three main improvements. First, a novel backbone network was designed by incorporating new convolutional layers and the Simplified Spatial Pyramid Pooling - Fast (SimSPPF) module to strengthen feature extraction. Second, a dynamic up-sampling technique was introduced to improve the model’s ability to recover fine details. Finally, a bidirectional feature pyramid network (BiFPN) was integrated to optimize feature fusion, leveraging a bidirectional information transfer mechanism and an adaptive feature selection strategy. A publicly available 5-class object mmWave radar heatmap dataset, including 2,500 annotated images, were selected for data modeling and method evaluation. The results show that the mean average precision (mAP), precision and recall of the S2DB-mmWave YOLOv8n model were 93.1% mAP@0.5, 55.8% mAP@0.5:0.95, 89.4% and 90.6%, respectively, which is 3.3, 1.6, 4.5 and 7.7 percentage points higher than the baseline YOLOv8n network without increasing the parameter count.</p></div>2025-09-19T21:26:24ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0332931.g005https://figshare.com/articles/figure/SimSPPF_module_structure_/30169068CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/301690682025-09-19T21:26:24Z |
| spellingShingle | SimSPPF module structure. Mengqi Yuan (4852555) Biological Sciences not elsewhere classified Information Systems not elsewhere classified three main improvements strengthen feature extraction recover fine details optimize feature fusion novel backbone network model &# 8217 mean average precision harsh weather conditions great practical significance 500 annotated images target detection scene new target detection xlink "> millimeter publicly available 5 map ), precision important research direction 6 %, respectively mmwave yolov8n model object detection mmwave yolov8n detection algorithm strong privacy sampling technique results show poor performance method evaluation low cost intelligent security including 2 deep learning data modeling classification framework |
| status_str | publishedVersion |
| title | SimSPPF module structure. |
| title_full | SimSPPF module structure. |
| title_fullStr | SimSPPF module structure. |
| title_full_unstemmed | SimSPPF module structure. |
| title_short | SimSPPF module structure. |
| title_sort | SimSPPF module structure. |
| topic | Biological Sciences not elsewhere classified Information Systems not elsewhere classified three main improvements strengthen feature extraction recover fine details optimize feature fusion novel backbone network model &# 8217 mean average precision harsh weather conditions great practical significance 500 annotated images target detection scene new target detection xlink "> millimeter publicly available 5 map ), precision important research direction 6 %, respectively mmwave yolov8n model object detection mmwave yolov8n detection algorithm strong privacy sampling technique results show poor performance method evaluation low cost intelligent security including 2 deep learning data modeling classification framework |