LSKA Network Structure.
<div><p>At present, the low-altitude economy is booming, and the application of drones has shown explosive growth, injecting new vitality into economic development. UAVs will face complex environmental perception and security risks when operating in low airspace. Accurate target detectio...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , , |
| Published: |
2025
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1852017392518955008 |
|---|---|
| author | Xinwei Wang (352488) |
| author2 | Yue Hu (201714) Qing Liang (654718) Yujie He (3068052) Li Zhou (54356) |
| author2_role | author author author author |
| author_facet | Xinwei Wang (352488) Yue Hu (201714) Qing Liang (654718) Yujie He (3068052) Li Zhou (54356) |
| author_role | author |
| dc.creator.none.fl_str_mv | Xinwei Wang (352488) Yue Hu (201714) Qing Liang (654718) Yujie He (3068052) Li Zhou (54356) |
| dc.date.none.fl_str_mv | 2025-08-21T18:01:57Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0327732.g006 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/LSKA_Network_Structure_/29963191 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified weight file size uav autonomous perception shown explosive growth insufficient feature capture injecting new vitality extract critical features enrich feature fusion efficient feature extraction enabling convolution operations 9 %, 4 sppf module incorporates scale feature processing mechanism captures long mean average precision improve detection accuracy algorithm introduces akconv model &# 8217 large convolution kernels improved algorithm achieves 33 %, respectively low altitude economy convolution kernels altitude economy detection precision original algorithm scale searches paper achieves lska mechanism improved yolov8s c2f module akconv allows accurate large detection recall 5 %, xlink "> thereby achieving study focuses security risks sampling shapes range dependencies precisely adapt perform rapid parameter count orderly operation model lightweighting low efficiency low airspace key support experimental results economic development |
| dc.title.none.fl_str_mv | LSKA Network Structure. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>At present, the low-altitude economy is booming, and the application of drones has shown explosive growth, injecting new vitality into economic development. UAVs will face complex environmental perception and security risks when operating in low airspace. Accurate target detection technology has become a key support to ensure the orderly operation of UAVs. This paper studies UAV target detection algorithm based on deep learning, in order to improve detection accuracy and speed, and meet the needs of UAV autonomous perception under the background of low altitude economy. This study focuses on the limitations of the YOLOv8s target detection algorithm, including its low efficiency in multi-scale feature processing and insufficient small target detection capability, which hinder its ability to perform rapid and accurate large-scale searches for drones. An improved target detection algorithm is proposed to address these issues. The algorithm introduces AKConv into the C2F module. AKConv allows for convolution kernels with arbitrary numbers and sampling shapes, enabling convolution operations to more precisely adapt to targets at different locations, thereby achieving more efficient feature extraction. To further enhance the model’s ability to extract critical features of small targets, the SPPF module incorporates the LSKA mechanism. This mechanism captures long-range dependencies and adaptivity more effectively while addressing computational complexity issues associated with large convolution kernels. Finally, the Bi-FPN feature pyramid network structure is introduced at the 18th layer of the model to accelerate and enrich feature fusion in the neck. Combined with the SCDown structure, a novel Bi-SCDown-FPN feature pyramid network structure is proposed, making it more suitable for detecting targets with insufficient feature capture in complex environments. Experimental results on the VisDrone2019 UAV dataset show that the improved algorithm achieves a 5.9%, 4.5%, and 6.1% increase in detection precision, detection recall, and mean average precision, respectively, compared to the original algorithm. Moreover, the parameter count and weight file size are reduced by 13.41% and 13.33%, respectively. Compared to other mainstream target detection algorithms, the proposed method demonstrates certain advantages. In summary, the target detection algorithm proposed in this paper achieves a dual improvement in model lightweighting and detection accuracy.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_bd3c533a5ce47a5259c9b2efd47d36d3 |
| identifier_str_mv | 10.1371/journal.pone.0327732.g006 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29963191 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | LSKA Network Structure.Xinwei Wang (352488)Yue Hu (201714)Qing Liang (654718)Yujie He (3068052)Li Zhou (54356)Space ScienceBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedweight file sizeuav autonomous perceptionshown explosive growthinsufficient feature captureinjecting new vitalityextract critical featuresenrich feature fusionefficient feature extractionenabling convolution operations9 %, 4sppf module incorporatesscale feature processingmechanism captures longmean average precisionimprove detection accuracyalgorithm introduces akconvmodel &# 8217large convolution kernelsimproved algorithm achieves33 %, respectivelylow altitude economyconvolution kernelsaltitude economydetection precisionoriginal algorithmscale searchespaper achieveslska mechanismimproved yolov8sc2f moduleakconv allowsaccurate largedetection recall5 %,xlink ">thereby achievingstudy focusessecurity riskssampling shapesrange dependenciesprecisely adaptperform rapidparameter countorderly operationmodel lightweightinglow efficiencylow airspacekey supportexperimental resultseconomic development<div><p>At present, the low-altitude economy is booming, and the application of drones has shown explosive growth, injecting new vitality into economic development. UAVs will face complex environmental perception and security risks when operating in low airspace. Accurate target detection technology has become a key support to ensure the orderly operation of UAVs. This paper studies UAV target detection algorithm based on deep learning, in order to improve detection accuracy and speed, and meet the needs of UAV autonomous perception under the background of low altitude economy. This study focuses on the limitations of the YOLOv8s target detection algorithm, including its low efficiency in multi-scale feature processing and insufficient small target detection capability, which hinder its ability to perform rapid and accurate large-scale searches for drones. An improved target detection algorithm is proposed to address these issues. The algorithm introduces AKConv into the C2F module. AKConv allows for convolution kernels with arbitrary numbers and sampling shapes, enabling convolution operations to more precisely adapt to targets at different locations, thereby achieving more efficient feature extraction. To further enhance the model’s ability to extract critical features of small targets, the SPPF module incorporates the LSKA mechanism. This mechanism captures long-range dependencies and adaptivity more effectively while addressing computational complexity issues associated with large convolution kernels. Finally, the Bi-FPN feature pyramid network structure is introduced at the 18th layer of the model to accelerate and enrich feature fusion in the neck. Combined with the SCDown structure, a novel Bi-SCDown-FPN feature pyramid network structure is proposed, making it more suitable for detecting targets with insufficient feature capture in complex environments. Experimental results on the VisDrone2019 UAV dataset show that the improved algorithm achieves a 5.9%, 4.5%, and 6.1% increase in detection precision, detection recall, and mean average precision, respectively, compared to the original algorithm. Moreover, the parameter count and weight file size are reduced by 13.41% and 13.33%, respectively. Compared to other mainstream target detection algorithms, the proposed method demonstrates certain advantages. In summary, the target detection algorithm proposed in this paper achieves a dual improvement in model lightweighting and detection accuracy.</p></div>2025-08-21T18:01:57ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0327732.g006https://figshare.com/articles/figure/LSKA_Network_Structure_/29963191CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/299631912025-08-21T18:01:57Z |
| spellingShingle | LSKA Network Structure. Xinwei Wang (352488) Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified weight file size uav autonomous perception shown explosive growth insufficient feature capture injecting new vitality extract critical features enrich feature fusion efficient feature extraction enabling convolution operations 9 %, 4 sppf module incorporates scale feature processing mechanism captures long mean average precision improve detection accuracy algorithm introduces akconv model &# 8217 large convolution kernels improved algorithm achieves 33 %, respectively low altitude economy convolution kernels altitude economy detection precision original algorithm scale searches paper achieves lska mechanism improved yolov8s c2f module akconv allows accurate large detection recall 5 %, xlink "> thereby achieving study focuses security risks sampling shapes range dependencies precisely adapt perform rapid parameter count orderly operation model lightweighting low efficiency low airspace key support experimental results economic development |
| status_str | publishedVersion |
| title | LSKA Network Structure. |
| title_full | LSKA Network Structure. |
| title_fullStr | LSKA Network Structure. |
| title_full_unstemmed | LSKA Network Structure. |
| title_short | LSKA Network Structure. |
| title_sort | LSKA Network Structure. |
| topic | Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified weight file size uav autonomous perception shown explosive growth insufficient feature capture injecting new vitality extract critical features enrich feature fusion efficient feature extraction enabling convolution operations 9 %, 4 sppf module incorporates scale feature processing mechanism captures long mean average precision improve detection accuracy algorithm introduces akconv model &# 8217 large convolution kernels improved algorithm achieves 33 %, respectively low altitude economy convolution kernels altitude economy detection precision original algorithm scale searches paper achieves lska mechanism improved yolov8s c2f module akconv allows accurate large detection recall 5 %, xlink "> thereby achieving study focuses security risks sampling shapes range dependencies precisely adapt perform rapid parameter count orderly operation model lightweighting low efficiency low airspace key support experimental results economic development |