بدائل البحث:
processing algorithm » modeling algorithm (توسيع البحث), routing algorithm (توسيع البحث), tracking algorithm (توسيع البحث)
fluent processing » light processing (توسيع البحث), query processing (توسيع البحث), text processing (توسيع البحث)
update algorithm » pass algorithm (توسيع البحث), data algorithms (توسيع البحث), ipca algorithm (توسيع البحث)
coding algorithm » cosine algorithm (توسيع البحث), modeling algorithm (توسيع البحث), finding algorithm (توسيع البحث)
element update » element data (توسيع البحث)
level coding » level according (توسيع البحث), level modeling (توسيع البحث), level using (توسيع البحث)
processing algorithm » modeling algorithm (توسيع البحث), routing algorithm (توسيع البحث), tracking algorithm (توسيع البحث)
fluent processing » light processing (توسيع البحث), query processing (توسيع البحث), text processing (توسيع البحث)
update algorithm » pass algorithm (توسيع البحث), data algorithms (توسيع البحث), ipca algorithm (توسيع البحث)
coding algorithm » cosine algorithm (توسيع البحث), modeling algorithm (توسيع البحث), finding algorithm (توسيع البحث)
element update » element data (توسيع البحث)
level coding » level according (توسيع البحث), level modeling (توسيع البحث), level using (توسيع البحث)
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181
Transformer Encoder network structure.
منشور في 2025"…On the KITTI dataset, our algorithm achieved 3D average detection accuracy (AP3D) of 81.15%, 62.02%, and 58.68% across three difficulty levels. …"
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182
Line chart of frame rate.
منشور في 2025"…On the KITTI dataset, our algorithm achieved 3D average detection accuracy (AP3D) of 81.15%, 62.02%, and 58.68% across three difficulty levels. …"
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183
The total loss and three-component loss.
منشور في 2025"…On the KITTI dataset, our algorithm achieved 3D average detection accuracy (AP3D) of 81.15%, 62.02%, and 58.68% across three difficulty levels. …"
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184
Improved upsampling module based on Transformer.
منشور في 2025"…On the KITTI dataset, our algorithm achieved 3D average detection accuracy (AP3D) of 81.15%, 62.02%, and 58.68% across three difficulty levels. …"
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185
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186
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187
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188
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189
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190
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192
Overall framework design.
منشور في 2025"…Our approach uses cross-project code clone detection to establish the ground truth for software reuse, identifying code clones across popular GitHub projects as indicators of potential reuse candidates. …"
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193
Gamma distribution of reuse.
منشور في 2025"…Our approach uses cross-project code clone detection to establish the ground truth for software reuse, identifying code clones across popular GitHub projects as indicators of potential reuse candidates. …"
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194
Top 5 correlated features based on reuse.
منشور في 2025"…Our approach uses cross-project code clone detection to establish the ground truth for software reuse, identifying code clones across popular GitHub projects as indicators of potential reuse candidates. …"
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195
Features with the top importance score.
منشور في 2025"…Our approach uses cross-project code clone detection to establish the ground truth for software reuse, identifying code clones across popular GitHub projects as indicators of potential reuse candidates. …"
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196
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197
S1 Graphical abstract -
منشور في 2025"…The software uses a shape-detection algorithm to single out and track the movement of pillars’ tips for the most common shapes of EHT platforms. …"
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198
Quantitative results on WEDU dataset.
منشور في 2024"…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …"
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199
Counting results on DRPD dataset.
منشور في 2024"…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …"
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200
Quantitative results on RFRB dataset.
منشور في 2024"…In comparative experiments on four plant datasets, MAR-YOLOv9 improved the mAP@0.5 accuracy by 39.18% compared to seven mainstream object detection algorithms, and by 1.28% compared to the YOLOv9 model. …"