Improvement of ablation experiment results for YOLOv7 modules.
<p>(a) The original YOLO model (b)YOLOv7+SimAM. (c)YOLOv7+SimAM+Pconv.</p>
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2025
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| الموضوعات: | |
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| _version_ | 1852023684318887936 |
|---|---|
| author | Guoqing Zhang (151441) |
| author2 | Jiandong Liu (6064643) Yongxiang Zhao (671364) Wei Luo (80175) Keyu Mei (20542959) Penggang Wang (6038210) Yubin Song (3205194) Xiaoliang Li (720274) |
| author2_role | author author author author author author author |
| author_facet | Guoqing Zhang (151441) Jiandong Liu (6064643) Yongxiang Zhao (671364) Wei Luo (80175) Keyu Mei (20542959) Penggang Wang (6038210) Yubin Song (3205194) Xiaoliang Li (720274) |
| author_role | author |
| dc.creator.none.fl_str_mv | Guoqing Zhang (151441) Jiandong Liu (6064643) Yongxiang Zhao (671364) Wei Luo (80175) Keyu Mei (20542959) Penggang Wang (6038210) Yubin Song (3205194) Xiaoliang Li (720274) |
| dc.date.none.fl_str_mv | 2025-01-10T20:14:09Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0316933.g012 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/Improvement_of_ablation_experiment_results_for_YOLOv7_modules_/28187220 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified unmanned aerial vehicles uavs ), utilizing transfer learning techniques suppressing irrelevant information reducing missed detections partially occluded targets offering valuable insights global economy expands deep sort algorithms deep sort algorithm become increasingly crucial ship monitoring scenarios model &# 8217 limited ship data deep sort model emphasizing salient features enhanced system achieves demonstrated robust performance improves detection accuracy ship detection model training stable features xlink "> waterway transportation unlinked tracks significant challenges partial convolution logistics sector irregularly shaped iou metric invalid regions growth presents feature extraction false negatives extensive evaluation diou metric detected objects critical reference computational demands artificial intelligence article introduces also reduces address issues |
| dc.title.none.fl_str_mv | Improvement of ablation experiment results for YOLOv7 modules. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>(a) The original YOLO model (b)YOLOv7+SimAM. (c)YOLOv7+SimAM+Pconv.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_f13d64d904473f7facd82f20b85cded0 |
| identifier_str_mv | 10.1371/journal.pone.0316933.g012 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28187220 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Improvement of ablation experiment results for YOLOv7 modules.Guoqing Zhang (151441)Jiandong Liu (6064643)Yongxiang Zhao (671364)Wei Luo (80175)Keyu Mei (20542959)Penggang Wang (6038210)Yubin Song (3205194)Xiaoliang Li (720274)Science PolicySpace ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedunmanned aerial vehiclesuavs ), utilizingtransfer learning techniquessuppressing irrelevant informationreducing missed detectionspartially occluded targetsoffering valuable insightsglobal economy expandsdeep sort algorithmsdeep sort algorithmbecome increasingly crucialship monitoring scenariosmodel &# 8217limited ship datadeep sort modelemphasizing salient featuresenhanced system achievesdemonstrated robust performanceimproves detection accuracyship detectionmodel trainingstable featuresxlink ">waterway transportationunlinked trackssignificant challengespartial convolutionlogistics sectorirregularly shapediou metricinvalid regionsgrowth presentsfeature extractionfalse negativesextensive evaluationdiou metricdetected objectscritical referencecomputational demandsartificial intelligencearticle introducesalso reducesaddress issues<p>(a) The original YOLO model (b)YOLOv7+SimAM. (c)YOLOv7+SimAM+Pconv.</p>2025-01-10T20:14:09ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0316933.g012https://figshare.com/articles/figure/Improvement_of_ablation_experiment_results_for_YOLOv7_modules_/28187220CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/281872202025-01-10T20:14:09Z |
| spellingShingle | Improvement of ablation experiment results for YOLOv7 modules. Guoqing Zhang (151441) Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified unmanned aerial vehicles uavs ), utilizing transfer learning techniques suppressing irrelevant information reducing missed detections partially occluded targets offering valuable insights global economy expands deep sort algorithms deep sort algorithm become increasingly crucial ship monitoring scenarios model &# 8217 limited ship data deep sort model emphasizing salient features enhanced system achieves demonstrated robust performance improves detection accuracy ship detection model training stable features xlink "> waterway transportation unlinked tracks significant challenges partial convolution logistics sector irregularly shaped iou metric invalid regions growth presents feature extraction false negatives extensive evaluation diou metric detected objects critical reference computational demands artificial intelligence article introduces also reduces address issues |
| status_str | publishedVersion |
| title | Improvement of ablation experiment results for YOLOv7 modules. |
| title_full | Improvement of ablation experiment results for YOLOv7 modules. |
| title_fullStr | Improvement of ablation experiment results for YOLOv7 modules. |
| title_full_unstemmed | Improvement of ablation experiment results for YOLOv7 modules. |
| title_short | Improvement of ablation experiment results for YOLOv7 modules. |
| title_sort | Improvement of ablation experiment results for YOLOv7 modules. |
| topic | Science Policy Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified unmanned aerial vehicles uavs ), utilizing transfer learning techniques suppressing irrelevant information reducing missed detections partially occluded targets offering valuable insights global economy expands deep sort algorithms deep sort algorithm become increasingly crucial ship monitoring scenarios model &# 8217 limited ship data deep sort model emphasizing salient features enhanced system achieves demonstrated robust performance improves detection accuracy ship detection model training stable features xlink "> waterway transportation unlinked tracks significant challenges partial convolution logistics sector irregularly shaped iou metric invalid regions growth presents feature extraction false negatives extensive evaluation diou metric detected objects critical reference computational demands artificial intelligence article introduces also reduces address issues |