Ablation experiments on custom dataset.
<div><p>Pyrotechnic detection has always been one of the critical issues in blasting safety. Due to the complex environment of blasting sites, irregular detonator wire postures, and the differences in object scales, making the detection of pyrotechnics more challenging. To address these...
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2025
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| _version_ | 1852021132827295744 |
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| author | LinNa Li (5665885) |
| author2 | Han Gao (486886) JunYi Lu (21162047) XiaoXiao Xu (21162050) |
| author2_role | author author author |
| author_facet | LinNa Li (5665885) Han Gao (486886) JunYi Lu (21162047) XiaoXiao Xu (21162050) |
| author_role | author |
| dc.creator.none.fl_str_mv | LinNa Li (5665885) Han Gao (486886) JunYi Lu (21162047) XiaoXiao Xu (21162050) |
| dc.date.none.fl_str_mv | 2025-04-22T17:39:26Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0318172.t007 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Ablation_experiments_on_custom_dataset_/28842788 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biochemistry Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified parallel attention mechanism novel convolutional block new convolutional block iou loss function improved algorithm based model &# 8217 improved model achieved varying aspect ratios experimental results show depthwise separable convolution yolov8 </ p improving feature extraction blasting sites based integrate wavelet convolution average precision increase separable convolution blasting sites model uses varying scales results indicate recall increase blasting safety voc2012 dataset scale convolutions paper proposes overlap calculations object scales neck network irregular shapes effective solution detonator wires detect objects critical issues complex environment complex backgrounds c2f module built dataset bounding boxes |
| dc.title.none.fl_str_mv | Ablation experiments on custom dataset. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <div><p>Pyrotechnic detection has always been one of the critical issues in blasting safety. Due to the complex environment of blasting sites, irregular detonator wire postures, and the differences in object scales, making the detection of pyrotechnics more challenging. To address these challenges, this paper proposes an improved algorithm based on a multi-scale parallel attention mechanism and wavelet-separable convolution, called WA-YOLO. First, we integrate wavelet convolution into depthwise separable convolution and propose a novel convolutional block (WSDConv, Wavelet Separable Depthwise Convolution). This new convolutional block is added to the model’s backbone, improving feature extraction while also lowering computational parameters. Furthermore, we introduce an improved Cross Stage Partial (CSP) structure by combining multi-scale convolutions with a parallel attention mechanism, embedding it into the C2f module of the neck network to improve the model’s ability to detect objects of varying scales in complex backgrounds. To tackle the detection accuracy drop caused by the irregular shapes and varying aspect ratios of detonator wires, the model uses the Wise-IoU loss function. This enhances the model’s generalization and robustness by improving the precision of overlap calculations for bounding boxes. The experimental results show that the improved model achieved an average precision increase of 12.6% on the self-built dataset, particularly with an average precision increase of 8.3% in the detection of detonators. Additionally, the model performance also improved on the VOC2012 dataset, with a recall increase of 1.3% and an average precision increase of 1.6%. These results indicate that the proposed model exhibits strong generalization capabilities, can work effectively across different datasets, and provides an effective solution to the challenges of target detection in blasting environments.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_ace77ed2fa4cdbd331ff94b2bd2e42e7 |
| identifier_str_mv | 10.1371/journal.pone.0318172.t007 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28842788 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Ablation experiments on custom dataset.LinNa Li (5665885)Han Gao (486886)JunYi Lu (21162047)XiaoXiao Xu (21162050)BiochemistrySpace ScienceEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedparallel attention mechanismnovel convolutional blocknew convolutional blockiou loss functionimproved algorithm basedmodel &# 8217improved model achievedvarying aspect ratiosexperimental results showdepthwise separable convolutionyolov8 </ pimproving feature extractionblasting sites basedintegrate wavelet convolutionaverage precision increaseseparable convolutionblasting sitesmodel usesvarying scalesresults indicaterecall increaseblasting safetyvoc2012 datasetscale convolutionspaper proposesoverlap calculationsobject scalesneck networkirregular shapeseffective solutiondetonator wiresdetect objectscritical issuescomplex environmentcomplex backgroundsc2f modulebuilt datasetbounding boxes<div><p>Pyrotechnic detection has always been one of the critical issues in blasting safety. Due to the complex environment of blasting sites, irregular detonator wire postures, and the differences in object scales, making the detection of pyrotechnics more challenging. To address these challenges, this paper proposes an improved algorithm based on a multi-scale parallel attention mechanism and wavelet-separable convolution, called WA-YOLO. First, we integrate wavelet convolution into depthwise separable convolution and propose a novel convolutional block (WSDConv, Wavelet Separable Depthwise Convolution). This new convolutional block is added to the model’s backbone, improving feature extraction while also lowering computational parameters. Furthermore, we introduce an improved Cross Stage Partial (CSP) structure by combining multi-scale convolutions with a parallel attention mechanism, embedding it into the C2f module of the neck network to improve the model’s ability to detect objects of varying scales in complex backgrounds. To tackle the detection accuracy drop caused by the irregular shapes and varying aspect ratios of detonator wires, the model uses the Wise-IoU loss function. This enhances the model’s generalization and robustness by improving the precision of overlap calculations for bounding boxes. The experimental results show that the improved model achieved an average precision increase of 12.6% on the self-built dataset, particularly with an average precision increase of 8.3% in the detection of detonators. Additionally, the model performance also improved on the VOC2012 dataset, with a recall increase of 1.3% and an average precision increase of 1.6%. These results indicate that the proposed model exhibits strong generalization capabilities, can work effectively across different datasets, and provides an effective solution to the challenges of target detection in blasting environments.</p></div>2025-04-22T17:39:26ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0318172.t007https://figshare.com/articles/dataset/Ablation_experiments_on_custom_dataset_/28842788CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/288427882025-04-22T17:39:26Z |
| spellingShingle | Ablation experiments on custom dataset. LinNa Li (5665885) Biochemistry Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified parallel attention mechanism novel convolutional block new convolutional block iou loss function improved algorithm based model &# 8217 improved model achieved varying aspect ratios experimental results show depthwise separable convolution yolov8 </ p improving feature extraction blasting sites based integrate wavelet convolution average precision increase separable convolution blasting sites model uses varying scales results indicate recall increase blasting safety voc2012 dataset scale convolutions paper proposes overlap calculations object scales neck network irregular shapes effective solution detonator wires detect objects critical issues complex environment complex backgrounds c2f module built dataset bounding boxes |
| status_str | publishedVersion |
| title | Ablation experiments on custom dataset. |
| title_full | Ablation experiments on custom dataset. |
| title_fullStr | Ablation experiments on custom dataset. |
| title_full_unstemmed | Ablation experiments on custom dataset. |
| title_short | Ablation experiments on custom dataset. |
| title_sort | Ablation experiments on custom dataset. |
| topic | Biochemistry Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified parallel attention mechanism novel convolutional block new convolutional block iou loss function improved algorithm based model &# 8217 improved model achieved varying aspect ratios experimental results show depthwise separable convolution yolov8 </ p improving feature extraction blasting sites based integrate wavelet convolution average precision increase separable convolution blasting sites model uses varying scales results indicate recall increase blasting safety voc2012 dataset scale convolutions paper proposes overlap calculations object scales neck network irregular shapes effective solution detonator wires detect objects critical issues complex environment complex backgrounds c2f module built dataset bounding boxes |